information filtering si650 : information retrieva l

2010 © University of Michigan

Information FilteringSI650: Information Retrieval

Winter 2010

School of InformationUniversity of Michigan

Many slides are from Prof. ChengXiang Zhai’s lecture

1


Lecture Plan• Filtering vs. Retrieval• Content-based filtering (adaptive filtering)• Collaborative filtering (recommender systems)

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Short vs. Long Term Info Need• Short-term information need (Ad hoc retrieval)

– “Temporary need”, e.g., info about used cars– Information source is relatively static – User “pulls” information– Application example: library search, Web search

• Long-term information need (Filtering)– “Stable need”, e.g., new data mining algorithms– Information source is dynamic– System “pushes” information to user– Applications: news filter, recommender systems

3


Examples of Information Filtering• News filtering• Email filtering• Movie/book recommenders• Literature recommenders • And many others …

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Content-based Filtering vs. Collaborative Filtering

• Basic filtering question: Will user U like item X?• Two different ways of answering it

– Look at what U likes – Look at who likes X

• Can be combined

characterize X content-based filtering

characterize U collaborative filtering

5


Content-based filtering is also called

1. “Adaptive Information Filtering” in TREC2. “Selective Dissemination of Information (SDI)in Library & Information ScienceCollaborative filtering is also called “Recommender Systems”

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Why Bother?• Why do we need recommendation?• Why should amazon provide recommendation?• Why does Netflix spend millions to improve their

recommendation algorithm?

• Why can’t we rely on a search engine for all the recommendation?

7

2010 © University of MichiganChris Anderson, ‘The Long Tail’, Wired, Issue 12.10 - October 2004

Need a way to discover items that match our interests & tastes among tens or hundreds of thousands

The Long Tail

8

http://www.wired.com/wired/archive/12.10/


Part I: Adaptive Filtering

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Adaptive Information Filtering• Stable & long term interest, dynamic info. source• System must make a delivery decision

immediately as a document “arrives”

FilteringSystem…

my interest:

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AIF vs. Retrieval & Categorization• Like retrieval over a dynamic stream of docs, but

(global) ranking is impossible• Like online binary categorization, but with no initial

training data and with limited feedback• Typically evaluated with a utility function

– Each delivered doc gets a utility value– Good doc gets a positive value– Bad doc gets a negative value– E.g., Utility = 3* #good - 2 *#bad (linear utility)

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A Typical AIF System

...BinaryClassifierUserInterestProfile

User

Doc Source

Accepted Docs

Initialization

Learning FeedbackAccumulated Docs

utility func.

User profile text

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Three Basic Problems in AIF• Making filtering decision (Binary classifier)

– Doc text, profile text yes/no• Initialization

– Initialize the filter based on only the profile text or very few examples

• Learning from– Limited relevance judgments (only on “yes” docs)– Accumulated documents

• All trying to maximize the utility

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Major Approaches to AIF• “Extended” retrieval systems

– “Reuse” retrieval techniques to score documents– Use a score threshold for filtering decision– Learn to improve scoring with traditional feedback– New approaches to threshold setting and learning

• “Modified” categorization systems– Adapt to binary, unbalanced categorization– New approaches to initialization– Train with “censored” training examples

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Difference between Ranking, Classification, and Recommendation

• What is the query?– Ranking: no query– Classification: labeled data (a.k.a, training data)– Recommendation: user + items

• What is the target?– Ranking: order of objects– Classification: labels of objects– Recommendation: Score? Label? Order?

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A General Vector-Space Approach

doc vector

profile vector

Scoring Thresholding

yes

no

FeedbackInformation

VectorLearning

ThresholdLearning

threshold

UtilityEvaluation

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Difficulties in Threshold Learning

36.5 R33.4 N32.1 R29.9 ?27.3 ?…...

=30.0

• Censored data• Little/none labeled data• Scoring bias due to

vector learning• Exploration vs.

Exploitation

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Threshold Setting in Extended Retrieval Systems

• Utility-independent approaches (generally not working well, not covered in this lecture)

• Indirect (linear) utility optimization– Logistic regression (score prob. of relevance)

• Direct utility optimization– Empirical utility optimization – Expected utility optimization given score distributions

• All try to learn the optimal threshold

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Logistic Regression (Robertson & Walker. 00)

• General idea: convert score of D to p(R|D)

• Fit the model using feedback data• Linear utility is optimized with a fixed prob. cutoff• But,

– Possibly incorrect parametric assumptions– No positive examples initially– Censored data and limited positive feedback– Doesn’t address the issue of exploration

)()|(log DsDRO

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Direct Utility Optimization• Given

– A utility function U(CR+ ,CR- ,CN+ ,CN-)– Training data D={<si, {R,N,?}>}

• Formulate utility as a function of the threshold and training data: U=F(,D)

• Choose the threshold by optimizing F(,D), i.e.,

),(maxarg DF

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Empirical Utility Optimization• Basic idea

– Compute the utility on the training data for each candidate threshold (score of a training doc)

– Choose the threshold that gives the maximum utility• Difficulty: Biased training sample!

– We can only get an upper bound for the true optimal threshold.

• Solutions:– Heuristic adjustment(lowering) of threshold – Lead to “beta-gamma threshold learning”

21


Score Distribution Approaches( Aramptzis & Hameren 01; Zhang & Callan 01)

• Assume generative model of scores p(s|R), p(s|N)

• Estimate the model with training data • Find the threshold by optimizing the expected

utility under the estimated model• Specific methods differ in the way of defining

and estimating the scoring distributions

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Gaussian-Exponential Distributions• P(s|R) ~ N(,2) p(s-s0|N) ~ E()

)()(

)|()|( 02

2

021 ss

s

eNsspeRsp

(From Zhang & Callan 2001)23


Score Distribution Approaches (cont.)

• Pros– Principled approach– Arbitrary utility– Empirically effective

• Cons– May be sensitive to the scoring function– Exploration not addressed

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Part II: Collaborative Filtering

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What is Collaborative Filtering (CF)?

• Making filtering decisions for an individual user based on the judgments of other users

• Inferring individual’s interest/preferences from that of other similar users

• General idea– Given a user u, find similar users {u1, …, um}– Predict u’s preferences based on the preferences of

u1, …, um

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CF: Assumptions• Users with a common interest will have similar

preferences • Users with similar preferences probably share the

same interest• Examples

– “interest is IR” “favor SIGIR papers”– “favor SIGIR papers” “interest is IR”

• Sufficiently large number of user preferences are available

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CF: Intuitions• User similarity (Paul Resnick vs. Rahul Sami)

– If Paul liked the paper, Rahul will like the paper– ? If Paul liked the movie, Rahul will like the movie– Suppose Paul and Rahul viewed similar movies in the

past six months …

• Item similarity– Since 90% of those who liked Star Wars also liked

Independence Day, and, you liked Star Wars– You may also like Independence Day

The content of items “didn’t matter”!28


Rating-based vs. Preference-based• Rating-based: User’s preferences are

encoded using numerical ratings on items– Complete ordering– Absolute values can be meaningful – But, values must be normalized to combine

• Preference-based: User’s preferences are represented by partial ordering of items (Learning to Rank!)– Partial ordering– Easier to exploit implicit preferences

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A Formal Framework for Rating

u1

u2

…

ui

...

um

Users: U

Objects: O

o1 o2 … oj … on

3 1.5 …. … 2

2

1

3

Xij=f(ui,oj)=?

?

The task

Unknown function f: U x O R

• Assume known f values for some (u,o)’s

• Predict f values for other (u,o)’s

• Essentially function approximation, like other learning problems

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Where are the intuitions?• Similar users have similar preferences

– If u u’, then for all o’s, f(u,o) f(u’,o)• Similar objects have similar user preferences

– If o o’, then for all u’s, f(u,o) f(u,o’)• In general, f is “locally constant”

– If u u’ and o o’, then f(u,o) f(u’,o’)– “Local smoothness” makes it possible to predict

unknown values by interpolation or extrapolation• What does “local” mean?

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Two Groups of Approaches• Memory-based approaches

– f(u, o) = g(u)(o) g(u’)(o) if u u’ (g =preference function)

– Find “neighbors” of u and combine g(u’)(o)’s • Model-based approaches (not covered)

– Assume structures/model: object cluster, user cluster, f’ defined on clusters

– f(u, o) = f’(cu, co)– Estimation & Probabilistic inference

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Memory-based Approaches (Resnick et al. 94)

• General ideas:– xij: rating of object j by user i– ni: average rating of all objects by user i– Normalized ratings: – Memory-based prediction

• Specific approaches differ in w(a, i) -- the distance/similarity between user a and i

m

iijdj viawkv

1

),(

m

i

iawk1

),(/1 aajaj nvx

iijij nxv

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User Similarity Measures• Pearson correlation coefficient (sum over

commonly rated items)

• Cosine measure

• Many other possibilities!

jiij

jaaj

jiijaaj

pnxnx

nxnxiaw

22 )()(

))((),(

n

jij

n

jaj

n

jijaj

c

xx

xxiaw

1

2

1

2

1),(

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Improving User Similarity Measures

(Breese et al. 98)

• Dealing with missing values: default ratings• Inverse User Frequency (IUF): similar to IDF• Case Amplification: use w(a, i)p, e.g., p=2.5

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A Network Interpretation• If your neighbors love it,

you are likely to love it• Closer neighbors make

a larger impact• Measure the closeness

by the items you and her liked/disliked

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Network based approaches• No clear notion of “distance”

now, but the “scores” are estimated based on some propagation process.

• Score propagation based on random walk

• Two approaches:– Starting from the user, how

likely/how long can I reach the object?

– Starting from the object, how likely/how long can I hit the user?

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Random Walk and Hitting Time

i

kA

jP = 0.7

P = 0.3

• Hitting Time– TA: the first time that the random

walk is at a vertex in A• Mean Hitting Time

– hiA: expectation of TA given that

the walk starts from vertex i

0.3

0.7

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Computing Hitting Time

i

kA

j

TA: the first time that the random walk is at a vertex in A

}0,:min{ tAXtT tA

A ifor ,1)( Vj

Ajhjip

Aih

A ifor ,0

Iterative Computation

hiA: expectation of TA given that the

walk starting from vertex i

A i

h = 0

hiA = 0.7 hj

A + 0.3 hkA + 1

0.7

0.7

Apparently, hiA = 0 for those

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Bipartite Graph and Hitting Time

Expected proximity of query i to the query A : hitting time of i A, hi

A

Bipartite Graph:- Edges between V1 and V2

- No edge inside V1 or V2

- Edges are weighted- e.g., V1 = query; V2 = Url

A

ijw(i, j) = 3

4

5

0.7

0.4V1 V2

7 1

)73(3),()(

idjiwjip

A

ij

4

5

0.7

0.4V1 V2

7 1

)13(3),()(

jdjiwijp

A

k

ij

4

5

0.7

0.4V1 V2

7 1

2

),(),()(Vj ji d

jkwd

jiwkip

• convert to a directed graph, even collapse one group

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Example: Query Suggestion

Welcome to the hotel california

Suggestions

hotel california

eagles hotel california

hotel california band

hotel california by the eagles

hotel california song

lyrics of hotel california

listen hotel california eagle


Generating Query Suggestion using Click Graph (Mei et al. 08)

42

Aaa

american airline

mexiana

www.aa.com

www.theaa.com/travelwatch/planner_main.jsp

en.wikipedia.org/wiki/Mexicana

300

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Query Url• Construct a (kNN)

subgraph from the query log data (of a predefined number of queries/urls)

• Compute transition probabilities p(i j)

• Compute hitting time hiA

• Rank candidate queries using hi

A

freq(q, url)


Result: Query Suggestion

Hitting time

wikipedia friends

friends tv show wikipedia

friends home page

friends warner bros

the friends series

friends official site

friends(1994)

Google

friendship

friends poem

friendster

friends episode guide

friends scripts

how to make friends

true friends

Yahoo

secret friends

friends reunited

hide friends

hi 5 friends

find friends

poems for friends

friends quotes

Query = friends

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Summary• Filtering is “easy”

– The user’s expectation is low – Any recommendation is better than none– Making it practically useful

• Filtering is “hard”– Must make a binary decision, though ranking is

also possible– Data sparseness– “Cold start”

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Example: NetFlix

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2010 © University of Michigan46

References (Adaptive Filtering)· General papers on TREC filtering evaluation

· D. Hull, The TREC-7 Filtering Track: Description and Analysis , TREC-7 Proceedings. · D. Hull and S. Robertson, The TREC-8 Filtering Track Final Report, TREC-8 Proceedings. · S. Robertson and D. Hull, The TREC-9 Filtering Track Final Report, TREC-9 Proceedings.· S. Robertson and I. Soboroff, The TREC 2001 Filtering Track Final Report, TREC-10

Proceedings

· Papers on specific adaptive filtering methods · Stephen Robertson and Stephen Walker (2000), Threshold Setting in Adaptive Filtering .

Journal of Documentation, 56:312-331, 2000

· Chengxiang Zhai, Peter Jansen, and David A. Evans, Exploration of a heuristic approach to threshold learning in adaptive filtering, 2000 ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'00), 2000. Poster presentation.

· Avi Arampatzis and Andre van Hameren The Score-Distributional Threshold Optimization for Adaptive Binary Classification Tasks , SIGIR'2001.

· Yi Zhang and Jamie Callan, 2001, Maximum Likelihood Estimation for Filtering Threshold, SIGIR 2001.

· T. Ault and Y. Yang, 2002, kNN, Rocchio and Metrics for Information Filtering at TREC-10, TREC-10 Proceedings.


References (Collaborative Filtering)

• Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," CSCW. 175-186.

• P Resnick, HR Varian, Recommender Systems, CACM 1997

• Cohen, W.W., Schapire, R.E., and Singer, Y. (1999) "Learning to Order Things", Journal of AI Research, Volume 10, pages 243-270.

• Freund, Y., Iyer, R.,Schapire, R.E., & Singer, Y. (1999). An efficient boosting algorithm for combining preferences. Machine Learning Journal. 1999.

• Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Articial Intelligence, pp. 43-52.

• Alexandrin Popescul and Lyle H. Ungar, Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, UAI 2001.

• N. Good, J.B. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl. "Combining Collaborative Filtering with Personal Agents for Better Recommendations." Proceedings AAAI-99. pp 439-446. 1999.

• Qiaozhu Mei, Dengyong Zhou, Kenneth Church. Query Suggestion Using Hitting Time, CIKM'08, pages 469-478

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information filtering si650 : information retrieva l

Documents

university of michiganpart

university of michiganaif

information retrievalwinter

x contentbased filtering

filteringstable need

retrievaltemporary need

user u

dynamic info