Information Filtering SI650: Information Retrieval

Transcription

1 Information Filtering SI650: Information Retrieval Winter 2010 School of Information University of Michigan Many slides are from Prof. ChengXiang Zhai s lecture 1

2 Lecture Plan Filtering vs. Retrieval Content-based filtering (adaptive filtering) Collaborative filtering (recommender systems) 2

3 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

4 Examples of Information Filtering News filtering filtering Movie/book recommenders Literature recommenders And many others 4

5 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

6 Content-based filtering is also called 1. Adaptive Information Filtering in TREC 2. Selective Dissemination of Information (SDI) in Library & Information Science Collaborative filtering is also called Recommender Systems 6

7 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

8 The Long Tail Need a way to discover items that match our interests & tastes among tens or hundreds of thousands Chris Anderson, The Long Tail, Wired, Issue October

9 Part I: Adaptive Filtering 9

10 Adaptive Information Filtering Stable & long term interest, dynamic info. source System must make a delivery decision immediately as a document arrives my interest: Filtering System 10

11 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) 11

12 A Typical AIF System Initialization User profile text... Doc Source Binary Classifier User Interest Profile Accepted Docs User utility func. Accumulated Docs Learning Feedback 12

13 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 13

14 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 14

15 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? 15

16 A General Vector-Space Approach doc vector Scoring Thresholding no yes Utility Evaluation profile vector threshold Vector Learning Threshold Learning Feedback Information 16

17 Difficulties in Threshold Learning 36.5 R 33.4 N 32.1 R 29.9? 27.3?... =30.0 Censored data Little/none labeled data Scoring bias due to vector learning Exploration vs. Exploitation 17

18 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 18

19 Logistic Regression (Robertson & Walker. 00) General idea: convert score of D to p(r D) logo( R D) s( 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 19

20 Given Direct Utility Optimization A utility function U(C R+,C R-,C N+,C N- ) Training data D={<s i, {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., arg max F(, D) 20

21 Basic idea Empirical Utility Optimization 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

22 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 22

23 Gaussian-Exponential Distributions P(s R) ~ N(, 2 ) p(s-s 0 N) ~ E() ) ( ) ( ) ( ) ( s s s e N s s p e R s p (From Zhang & Callan 2001) 23

24 Score Distribution Approaches (cont.) Pros Principled approach Arbitrary utility Empirically effective Cons May be sensitive to the scoring function Exploration not addressed 24

25 Part II: Collaborative Filtering 25

26 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 {u 1,, u m } Predict u s preferences based on the preferences of u 1,, u m 26

27 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 27

28 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

29 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 29

30 A Formal Framework for Rating Objects: O Users: U o 1 o 2 o j o n X ij =f(u i,o j )=? u 1 u 2 u i... u m ? Unknown function f: U x O R The task Assume known f values for some (u,o) s Predict f values for other (u,o) s Essentially function approximation, like other learning problems 30

31 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? 31

32 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 (c u, c o ) Estimation & Probabilistic inference 32

33 General ideas: Memory-based Approaches (Resnick et al. 94) x ij : rating of object j by user i n i : average rating of all objects by user i Normalized ratings: Memory-based prediction v k m w( a, i) k / w( a, i) dj v ij i1 m 1 xaj vaj na i1 Specific approaches differ in w(a, i) -- the distance/similarity between user a and i v ij x ij n i 33

34 User Similarity Measures Pearson correlation coefficient (sum over commonly rated items) Cosine measure Many other possibilities! j i ij j a aj j i ij a aj p n x n x n x n x i a w 2 2 ) ( ) ( ) )( ( ), ( n j ij n j aj n j ij aj c x x x x i a w ), ( 34

35 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 35

36 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 36

37 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? 37

38 Random Walk and Hitting Time P = k A i P = j Hitting Time T A : the first time that the random walk is at a vertex in A Mean Hitting Time h ia : expectation of T A given that the walk starts from vertex i 38

39 Computing Hitting Time h i A = 0.7 h j A h k A i 0.7 k j h = 0 A T A : the first time that the random walk is at a vertex in A T A min{ t : X t A, t 0} h ia : expectation of T A given that the walk starting from vertex i Apparently, h i A = 0 for those ia A h i jv A p( i j) 1, for ia h j 0, for ia Iterative Computation 39

40 A i Bipartite Graph and Hitting Time V 1 k V w(i, j) = 3 w( i, j) 3 p( j i) dw j ( i, j) (3w ( 1) k, j) p( i k) w( i, j) 3 jv 2 p( di i j) d j (3 7) j d i Bipartite Graph: - Edges between V 1 and V 2 - No edge inside V 1 or V 2 - Edges are weighted - e.g., V1 = query; V2 = Url Expected proximity of query i to the query A : hitting time of i A, h i A convert to a directed graph, even collapse one group 40

41 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

42 Generating Query Suggestion using Click Query aa american airline A mexiana freq(q, url) Graph (Mei et al. 08) Url planner_main.jsp en.wikipedia.org/wiki/mexicana 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 h i A Rank candidate queries using h i A 42

43 Query = friends Result: Query Suggestion 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 Hitting time wikipedia friends friends tv show wikipedia friends home page friends warner bros the friends series friends official site friends(1994) 43

44 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 44

45 Example: NetFlix 45

46 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: , 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), 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 T. Ault and Y. Yang, 2002, knn, Rocchio and Metrics for Information Filtering 46 at TREC-10, TREC-10 Proceedings.

47 References (Collaborative Filtering) Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," CSCW 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 Freund, Y., Iyer, R.,Schapire, R.E., & Singer, Y. (1999). An efficient boosting algorithm for combining preferences. Machine Learning Journal 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 Alexandrin Popescul and Lyle H. Ungar, Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, UAI 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 Qiaozhu Mei, Dengyong Zhou, Kenneth Church. Query Suggestion Using Hitting Time, CIKM'08, pages