An Approach to Cover More Advertisers in Adwords

ABudhiraja and P Krishna Reddyamarbudhirajaresearchiiitacin

pkreddyiiitacin

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Introduction

Search engines have been deemed as the starting point of most of the web transactions

This results in a significantly larger user base making search engines an ideal avenue for businesses to reach potential consumers

In the meta-model of knowledge sharing of search engines advertising has become a major revenue earning source for their sustenance

According to IAB standards sponsored search is the most dominant form of online advertising covering almost 43 of the entire market

Introduction Sponsored Search

The model of search engine advertising is more popularly known as Adwords

When a user queries a search engine a list of search results and sponsored results or advertisements are displayed

Advertisers bid on search keywords and pay the search engine according to Pay Per Click (PPC) model to display the advertisement on the query page containing the desired keywords

Introduction Problem StatementSearch keywords follow a long tail frequency distribution with

a small and fat head of highly frequent keywords

a long but thin tail of less frequent keywords

During the keyword auctions there is a high competition for head keywords while there is little to no competition for the tail keywords

This leads to underutilization of ad space of a large number of tail keywords Also it leads to ignorance of a diverse set of potential consumers who could be captured by targeting tail keywords

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Background Model of AdwordsThe present model is considered as

the online bipartite graph matching with advertisers as one disjoint set and incoming queries are the other

When a new query comes in it is to be matched to a set of advertisers

The advertisers are then ranked and their ads are display in that ranked order

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Introduction

Search engines have been deemed as the starting point of most of the web transactions

This results in a significantly larger user base making search engines an ideal avenue for businesses to reach potential consumers

In the meta-model of knowledge sharing of search engines advertising has become a major revenue earning source for their sustenance

According to IAB standards sponsored search is the most dominant form of online advertising covering almost 43 of the entire market

Introduction Sponsored Search

The model of search engine advertising is more popularly known as Adwords

When a user queries a search engine a list of search results and sponsored results or advertisements are displayed

Advertisers bid on search keywords and pay the search engine according to Pay Per Click (PPC) model to display the advertisement on the query page containing the desired keywords

Introduction Problem StatementSearch keywords follow a long tail frequency distribution with

a small and fat head of highly frequent keywords

a long but thin tail of less frequent keywords

During the keyword auctions there is a high competition for head keywords while there is little to no competition for the tail keywords

This leads to underutilization of ad space of a large number of tail keywords Also it leads to ignorance of a diverse set of potential consumers who could be captured by targeting tail keywords

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Background Model of AdwordsThe present model is considered as

the online bipartite graph matching with advertisers as one disjoint set and incoming queries are the other

When a new query comes in it is to be matched to a set of advertisers

The advertisers are then ranked and their ads are display in that ranked order

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Introduction Sponsored Search

The model of search engine advertising is more popularly known as Adwords

When a user queries a search engine a list of search results and sponsored results or advertisements are displayed

Advertisers bid on search keywords and pay the search engine according to Pay Per Click (PPC) model to display the advertisement on the query page containing the desired keywords

Introduction Problem StatementSearch keywords follow a long tail frequency distribution with

a small and fat head of highly frequent keywords

a long but thin tail of less frequent keywords

During the keyword auctions there is a high competition for head keywords while there is little to no competition for the tail keywords

This leads to underutilization of ad space of a large number of tail keywords Also it leads to ignorance of a diverse set of potential consumers who could be captured by targeting tail keywords

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Background Model of AdwordsThe present model is considered as

the online bipartite graph matching with advertisers as one disjoint set and incoming queries are the other

When a new query comes in it is to be matched to a set of advertisers

The advertisers are then ranked and their ads are display in that ranked order

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Introduction Problem StatementSearch keywords follow a long tail frequency distribution with

a small and fat head of highly frequent keywords

a long but thin tail of less frequent keywords

During the keyword auctions there is a high competition for head keywords while there is little to no competition for the tail keywords

This leads to underutilization of ad space of a large number of tail keywords Also it leads to ignorance of a diverse set of potential consumers who could be captured by targeting tail keywords

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Background Model of AdwordsThe present model is considered as

the online bipartite graph matching with advertisers as one disjoint set and incoming queries are the other

When a new query comes in it is to be matched to a set of advertisers

The advertisers are then ranked and their ads are display in that ranked order

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Background Model of AdwordsThe present model is considered as

the online bipartite graph matching with advertisers as one disjoint set and incoming queries are the other

When a new query comes in it is to be matched to a set of advertisers

The advertisers are then ranked and their ads are display in that ranked order

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Model of AdwordsThe present model is considered as

the online bipartite graph matching with advertisers as one disjoint set and incoming queries are the other

When a new query comes in it is to be matched to a set of advertisers

The advertisers are then ranked and their ads are display in that ranked order

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Adwords Architecture

1 Analyze Query

2 Retrieve Relevant Advertisers

3 Bidding

4 Ranking Advertisers

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Coverage Patterns- Central Idea

The basic idea of Coverage Pattern is inspired from the set cover problem in set theory

Given a universe U and a family S of subsets of U a cover is a subfamily C S subof sets whose union is U

Using the same notion coverage patterns aim to identify items that cover certain percentage of the entire data

A keypoint to be mentioned is that coverage patterns aim at identifying that usually ldquodo notrdquo occur together in contrast to frequent patterns that identify patterns in data that occur together

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Coverage Patterns - Notations

Let W be a set of webpages of a website W = w1 w2 hellip wN

Let D be a set of transactions from the click stream data D = T1 T2 hellip such that T Wsub

X is defined as a patterns of webpages such that X W X = subwp wq hellip

Twi denotes the set of transactions containing the webpage wi and its cardinality is denoted as |Twi|

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Coverage Patterns - Definitions

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Coverage Patterns - Definitions

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Coverage Patterns

A pattern is interesting if it has a high CS and low OR

A high CS value indicates more number of visitors and a low OR value means less repetitions amongst the visitors

A pattern is said to be interesting if CS(X) gt minCS(X) OR(X) lt maxOR and RF(wi) gt minRF

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Background Coverage Patterns - Example

Dataset

Assuming minRF - 02 minCS - 03 and maxOR- to be 05

Ta is 5 Tb is 7 and f Tf is 1 So RF for a is 05 for b is 07 and for f is 01

RF(f) = 01 lt 02 (minRF) f will be removed RF(a) = 05 gt 02and RF(b) = 07 gt 02 so a and b are not removed

ba is a candidate pattern (Order in a pattern is decreasing order of the RF)

The Coverage Set for ba is 12345678910 and |CSet ba| is 10 Hence CS = 1010 = 1 gt 03 (minCS)

The transactions containing ba together are 110 and Ta = 5 so the overlap ratio is 25 = 04 lt maxOR is a coverage pattern

Since pattern ba has CS gt minCS and OR gt maxOR and a and b satisfy the minRF requirements ba is a coverage pattern

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Proposed Approach Basic IdeaBecause of the nature of distribution of search keywords there is very less

competition for tail keywords As a result there are very less or no advertisers for such keywords

We noticed that if could combine such keywords into groups such that these keyword groups have a certain number of visitors we can utilize the ad space of such keywords

To perform the grouping of search keywords we employ the notion of query taxonomy to group semantically similar words

These groups are further mined from the logs in the form of coverage patterns

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Proposed ModelWe propose to add a middle layer of

coverage patterns to the bipartite model of Adwords

In the proposed model the incoming queries are first matched to a coverage pattern using the concept taxonomy

The coverage pattern is then matched to a set of advertisers

The advertisers are then ranked according to their bids and the ads are displayed

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Proposed Architecture

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Architecture ComparisonStep Modification with respect to bipartite

architecture

Analyze Query This step remains the same except that the subconcept of the query is also retrieved

Retrieve RelevantAds From the

Matching

In this step the advertisers who have been matched to the coverage pattern containing the

subconcept of the query are retrieved (The matching between coverage patterns and

advertisers would be explained later)

Bidding Stays the same

Ranking Advertisers Stays the same

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Coverage Pattern and Advertisers MatchingCoverage Pattern and Advertiser matching is the most important phase in the

architecture

This has been further divided into four steps

a Converting Query Logs to Concept Transactions

b Extraction of Coverage Patterns

c Estimation of Number of Impressions for Advertisers

d Matching Coverage Patterns and Advertisers

Each step is explained in the later slides

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Step 1 Converting Query Logs to Concept Transactions

One key point to note here is that the web query logs cannot be directly mined for coverage patterns because of the large vocabulary size (even when we only consider English)

To generalize the coverage pattern mining we proposed to use a three-level concept taxonomy to classify queries into a pair of concept and subconcept

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Step 1Converting Query Logs to Concept Transactions (cntd)

Using the same taxonomy we convert the web query logs into concept transactions using the techniques of query classification

To define a transaction we consider a session boundary of 30 mins in the query logs for each user

Sample Sessions

Converted Concepted Transactions

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Step 2 Extraction of Coverage Patterns

Coverage patterns are extracted from the converted concept transactions

One key point to note is that coverage patterns mine unique visitors while the standard models of advertising are either based on Impressions or Clicks

So we convert coverage patternsrsquo coverage into number of impressions as follows

For the above example we consider the concept of Science and Agriculture Biology Chemistry Environment Physics and Technology as its subconcepts

The transaction size is also assumed to be 1000 NOTE We also rank the coverage patterns in

ascending order of their CS - OR parameter

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Step 3 Estimating Required Impressions for Advertisers

In Adwords advertisers create an ad campaign for their website

In an ad campaign a daily budget and a bid is specified on the keywords that they chose to bid upon

Using CTR bid and daily budget values we calculated the number of impressions it will take to exhaust the budget of an advertiser using the following identity

The above table shows details of nine advertisers who bid upon the concept of Science

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Step 4 Matching Advertisers to Coverage Patterns

With coverage patterns and advertisers in the same unit of number of impressions we can create a matching between the two

The matching can be termed as a MANY-TO-ONE matching between coverage patterns and advertisers because a coverage patterns covers multiple keywords from different nodes in the taxonomy

The matching algorithm has a relaxation parameter ε to perform faster

The algorithms loops over coverage patterns and then advertisers and a coverage pattern is allocated to an advertisers if the following condition is satisfied

AdImpressions - 1048576 CPCoverage lt ε times AdImpressions

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Experiments DatasetWe performed a comparative study on the bipartite model of Adwords with and

without coverage patterns layers

We used AOL search query dataset to run the experiments

We took the most popular four categories of queries to run our experiments

Query Dataset for the most popular four categories

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Experiments Performance Metrics

1 Number of Advertisements per Session (AS) as the ratio of Sum of Unique Advertisements of all Sessions (SUAS) and Number of Sessions with Advertisements (NSA) to indicate the utilization of a session Higher value of AS indicates better use of ad space

AS = SUAS NAS

2 An increase in diversity among the viewers of the advertisements was also observed To indicate the same the value of Sessions per Advertisement (SA) which is the ratio of Number of Advertisements of all Sessions (NAS) to Number of Advertisements (NA) Higher value of metric implies the more number of unique eyeballs and thus increasing the chances of the advertisement being clicked by diverse users

SA = NASNA

Experiments Performance Metrics

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results- Utilization of Ad Space

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Graphs show a comparison of the bipartite Adwords system - With and Without Coverage Patterns layer

Experiments Results - Diversity

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Related WorkMost works in Adwords target algorithms to optimize different aspects of the

system including revenue welfarism and display of ads

Another aspect that is targeted in Adwords is the bidding scenarios with respect to Adwords Several studies have touched up on how to increase revenue in dynamic bidding scenario when you only have a partial information of the system

In this paper we have proposed an architectural solution to use the ad space of the search keywords Bidding strategies and Budget optimization can be placed on top of it

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Outline

Introduction

Background

Proposed Approach

Experiments

Related Work

Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work

Conclusions and Future Work

In this paper an architectural solution is proposed for Adwords to utilize the ad space of tail keywords The proposed approach also considerable improvement with respect to diversity in reach of advertisements

We plan on investigating the coverage patterns approach with respect to different taxonomies We believe a hybrid taxonomy would be the best when it comes to Adwords architecture

We also plan on expanding the user boundaries of exploration in searching beyond search sessions We plan on extracting user goals and modelling the transactions from them

• An Approach to Cover More Advertisers in Adwords
• Outline
• Introduction
• Introduction Sponsored Search
• Introduction Problem Statement
• Outline (2)
• Background Model of Adwords
• Background Adwords Architecture
• Background Coverage Patterns- Central Idea
• Background Coverage Patterns - Notations
• Background Coverage Patterns - Definitions
• Background Coverage Patterns - Definitions
• Background Coverage Patterns
• Background Coverage Patterns - Example
• Outline (3)
• Proposed Approach Basic Idea
• Proposed Model
• Proposed Architecture
• Architecture Comparison
• Outline (4)
• Coverage Pattern and Advertisers Matching
• Step 1 Converting Query Logs to Concept Transactions
• Step 1Converting Query Logs to Concept Transactions (cntd)
• Step 2 Extraction of Coverage Patterns
• Step 3 Estimating Required Impressions for Advertisers
• Step 4 Matching Advertisers to Coverage Patterns
• Outline (5)
• Experiments Dataset
• Experiments Performance Metrics (2)
• Experiments Results- Utilization of Ad Space
• Experiments Results - Diversity
• Outline (6)
• Related Work
• Outline (7)
• Conclusions and Future Work