recommendation systems: applying amazon's collaborative filtering methods to e-commerce

Amazon Recommendation Services 123Mua.vn Recommendation Services

Recommendation Services

Nguyen.Cao-Duc

Data Mining Team LeadE-Commerce & Services Dept.

VNG Corp.

Internal Research Document

September 19, 2012


Outline

1 Amazon Recommendation ServicesBusiness ModelResearch ModelImplementation Model

2 123Mua.vn Recommendation ServicesBusiness ModelImplementation Model


Business Model

Amazon Website


Business Model

Recommendation Services (cont.)

Browsing Product Recommendations:


Business Model


Viewing Product Recommendations:


Business Model


Purchasing Product Recommendations:


Business Model


How to have such Recommendations:


Business Model


Other Recommendations:


Research Model

Recommendation Problem

The main purpose of the recommendation system is torecommend personalized products to users of a merchant’sWeb site.

Two types of Recommendations:Content-based Filtering

Recommend items with similar content.Collaborative Filtering

Recommend items based on interests of a community ofusers.

Hybrid Content-based Collaborative FilteringCombination the two above approaches to overcome thedisadvantages of each approach.


Research Model

Content-based Recommendation


Research Model

Collaborative Filtering - Problem Description

Question: What should be the rating of Sam for Yellow?Approach: Use ratings of other users (user-based CF) orother items (item-based CF)


Research Model

How Collaborative Filtering works?


Research Model

Similarity Computation

Vector Cosine-based Similarity:Formular:

wu,v =

∑i∈I ru,i rv ,i√∑

i∈I r2u,i

√∑i∈I r2

v ,i

wi,j =

∑u∈U ru,i ru,j√∑

u∈U r2u,i

√∑u∈U r2

u,j

where:I is the set of items that both user u and v have rated.U is the set of users who rate both item i and i .Drawbacks

Different users have their own rating scales.


Research Model

Similarity Computation (cont.)

Correlation-based Similarity:Formular:

wu,v =

∑i∈I(ru,i − r̄u)(rv ,i − r̄v )√∑

i∈I(ru,i − r̄u)2√∑

i∈I(rv ,i − r̄v )2

wi,j =

∑u∈U(ru,i − r̄i)(ru,j − r̄j)√∑

u∈U(ru,i − r̄i)2√∑

u∈U(ru,j − r̄j)2

where:I is the set of items that both user u and v have rated.U is the set of users who rate both item i and i .


Research Model

Collaborative Recommendation

Given a user u:User-based prediction:

Aggregate the ratings of other users:

Pu,i = r̄u +

∑v∈V (rv ,i − r̄v )wu,v∑

v∈V |wu,v |

where V is the set of all users have rated the item iItem-based prediction:

Simple weighted average:

Pu,i =

∑n∈N ru,nwi,n∑

n∈N |wi,n|

where N is the set of other rated items of user u


Research Model

Collaborative Filtering - Drawbacks

User has to rate items to build profiles as well as item hasto be rated (cold-start problem: new user, new item, newsystem)Recommendations may not be diversed


Implementation Model

Recommendation Service Components



Recommendation Service Components

Recommendation Service Components takes Items ofKnown Interest of the given User and Similar Items Table tocreate Recommendation Items.



Recommendation Engine



Recommendation Service Components (cont.)

Sources of Items of Known Interest with respect to a User:User shopping card activitiesUser purchasing activitiesUser favorite items profile (i.e WishList)

Popular items are items satisfied some pre-specified popularcriteria:

Number of item viewsTime on item viewNumber of item purchasings




Each cell in the Similar Items Table associates a commonalityindex (CI) to indicate the relatedness of that item with thepopular item. The relatedness of two items i , j could beexpressed via:

Two items have been purchased togetherTwo items have been rated similarly

or the value of wi,j =∑

u∈U (ru,i−r̄i )(ru,j−r̄j )√∑u∈U (ru,i−r̄i )2

√∑u∈U (ru,j−r̄j )2

or the similarity between two items using content-basedfilteringor . . . combinations of all above with some controlledparameters.




Similar lists are combined appropriately by a weightingscheme representing the relative importance of popularitems with respect to the items of known interests.Weighting scheme of similar item lists:

Rating of the user to the popular item.User purchased multiple copies of the popular itemTime user spend on the popular itemRecent purchasing items are weighted more than earlierpurchasing items




The combined sorted list of similar items lists may need bemodified to remove certain items:

Items already purchased or rated by user or have beenviewed by user and its content has not changed.Items not in any product groups registered by user.

The combined sorted list of similar items lists may need bemodified to add certain items:

Items user has considered to purchase but did notpurchase.Items user has viewed but its content has changed afterthat.

The recommendation result may be transfered to the enduser by different types of transmission methods (view onsite, email, mobile message, chat message, etc.)


Business Model

123Mua.vn Website


Business Model

Paid services


Business Model

Recommendation Services


Business Model

Pageview Traffic


Business Model

Pageview Traffic (cont.)

We want to have:



Recommendation Engine



Recommendation Engine (cont.)



Items of Known Interest

Sources of Items of Known Interest with respect to a User:User category browsing or item viewing activitiesUser shopping card activities



Similar Item List



Similar Item List (cont.)

Sources of Similar Item List by targeting to certain itemsbased on:

Number of page views on an item and/or category of theitem and/or shop of itemTime on an item and/or category of item and/or shop ofitemBounce Rate on that itemExit Rate on that itemor items follow certain business objectives such as itemsis Up within a period of time.

Sources of Similar Item List with respect to a Popular item:Items of the same category and/or shop



Similar Item List (cont.)

Each item in the Similar Item List associates a commonalityindex (CI) to indicate the relatedness of that item with thepopular item. The relatedness of two items i , j could beexpressed via:

The value of wi,j =∑

u∈U (ru,i−r̄i )(ru,j−r̄j )√∑u∈U (ru,i−r̄i )2

√∑u∈U (ru,j−r̄j )2

where:ru,i represents interest level of user u towards item ior the similarity between two items using content-basedfilteringor . . . combinations of all above with some controlledparameters.



Weighting Scheme of Similar Item Lists

Similar item lists are combined appropriately by aweighting scheme representing the relative importance ofpopular items with respect to the items of known interests.Weighting scheme of similar item lists of popular items:

User views the popular item multiple timesTime user spent on the popular itemRecent viewings of popular items weighted more



Question

How to identify popular topics from multiplerelated/independent properties?How to measure the interest of a topic viewed by a user?



THANK YOU *-*

recommendation systems: applying amazon's collaborative filtering methods to e-commerce

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