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An Interactive Learning Approach to Optimizing Information Retrieval Systems

This presentation by Yisong Yue from Carnegie Mellon University explores advanced techniques in optimizing information retrieval systems using interactive learning approaches. It highlights the challenges of biased click data, the explore/exploit dilemma, and the evaluation of ranking functions out of a thousand possibilities. Key technical discussions include interpreting clicks, defining utility functions, and strategies for showing results to users. Various models, including the Dueling Bandits Problem and interleaving strategies, are examined in detail to enhance retrieval effectiveness.

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An Interactive Learning Approach to Optimizing Information Retrieval Systems

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  1. An Interactive Learning Approach to Optimizing Information Retrieval Systems CMU ML Lunch September 27th, 2010 Yisong Yue Carnegie Mellon University

  2. Information Systems

  3. Interactive Learning Setting • Find the best ranking function (out of 1000) • Show results, evaluate using click logs • Clicks biased (users only click on what they see) • Explore / exploit problem

  4. Interactive Learning Setting • Find the best ranking function (out of 1000) • Show results, evaluate using click logs • Clicks biased (users only click on what they see) • Explore / exploit problem • Technical issues • How to interpret clicks? • What is the utility function? • What results to show users?

  5. Interactive Learning Setting • Find the best ranking function (out of 1000) • Show results, evaluate using click logs • Clicks biased (users only click on what they see) • Explore / exploit problem • Technical issues • How to interpret clicks? • What is the utility function? • What results to show users?

  6. Team-Game Interleaving (u=thorsten, q=“svm”) f1(u,q)  r1 f2(u,q)  r2 NEXTPICK 1. Kernel Machines http://svm.first.gmd.de/ 2. SVM-Light Support Vector Machine http://ais.gmd.de/~thorsten/svm light/ 3. Support Vector Machine and Kernel ... References http://svm.research.bell-labs.com/SVMrefs.html 4. Lucent Technologies: SVM demo applet http://svm.research.bell-labs.com/SVT/SVMsvt.html 5. Royal Holloway Support Vector Machine http://svm.dcs.rhbnc.ac.uk 1. Kernel Machines http://svm.first.gmd.de/ 2. Support Vector Machinehttp://jbolivar.freeservers.com/ 3. An Introduction to Support Vector Machineshttp://www.support-vector.net/ 4. Archives of SUPPORT-VECTOR-MACHINES ...http://www.jiscmail.ac.uk/lists/SUPPORT... 5. SVM-Light Support Vector Machine http://ais.gmd.de/~thorsten/svm light/ Interleaving(r1,r2) 1. Kernel Machines T2http://svm.first.gmd.de/ 2. Support Vector Machine T1http://jbolivar.freeservers.com/ 3. SVM-Light Support Vector Machine T2http://ais.gmd.de/~thorsten/svm light/ 4. An Introduction to Support Vector Machines T1http://www.support-vector.net/ 5. Support Vector Machine and Kernel ... ReferencesT2 http://svm.research.bell-labs.com/SVMrefs.html 6. Archives of SUPPORT-VECTOR-MACHINES ... T1http://www.jiscmail.ac.uk/lists/SUPPORT... 7. Lucent Technologies: SVM demo applet T2http://svm.research.bell-labs.com/SVT/SVMsvt.html Invariant: For all k, in expectation same number of team members in top k from each team. • Interpretation: (r1 Âr2) ↔ clicks(T1) > clicks(T2) [Radlinski, Kurup, Joachims; CIKM 2008]

  7. Setting • Find the best ranking function (out of 1000) • Evaluate using click logs • Clicks biased (users only click on what they see) • Explore / exploit problem • Technical issues • How to interpret clicks? • What is the utility function? • What results to show users?

  8. Interleave A vs B …

  9. Interleave A vs C …

  10. Interleave B vs C …

  11. Interleave A vs B …

  12. Dueling Bandits Problem • Given K bandits b1, …, bK • Each iteration: compare (duel) two bandits • E.g., interleaving two retrieval functions [Yue & Joachims, ICML 2009] [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  13. Dueling Bandits Problem • Given K bandits b1, …, bK • Each iteration: compare (duel) two bandits • E.g., interleaving two retrieval functions • Cost function (regret): • (bt, bt’) are the two bandits chosen • b* is the overall best one • (% users who prefer best bandit over chosen ones) [Yue & Joachims, ICML 2009] [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  14. Example 1 • P(f* > f) = 0.9 • P(f* > f’) = 0.8 • Incurred Regret = 0.7 • Example 2 • P(f* > f) = 0.7 • P(f* > f’) = 0.6 • Incurred Regret = 0.3 • Example 3 • P(f* > f) = 0.51 • P(f* > f) = 0.55 • Incurred Regret = 0.06

  15. Assumptions • P(bi > bj) = ½ + εij (distinguishability) • Strong Stochastic Transitivity • For three bandits bi > bj > bk : • Monotonicity property • Stochastic Triangle Inequality • For three bandits bi > bj > bk : • Diminishing returns property • Satisfied by many standard models • E.g., Logistic / Bradley-Terry

  16. Explore then Exploit • First explore • Try to gather as much information as possible • Accumulates regret based on which bandits we decide to compare • Then exploit • We have a (good) guess as to which bandit best • Repeatedly compare that bandit with itself • (i.e., interleave that ranking with itself)

  17. Naïve Approach • In deterministic case, O(K) comparisons to find max • Extend to noisy case: • Repeatedly compare until confident one is better • Problem: comparing two awful (but similar) bandits • Example: • P(A > B) = 0.85 • P(A > C) = 0.85 • P(B > C) = 0.51 • Comparing B and C requires many comparisons!

  18. Interleaved Filter • Choose candidate bandit at random ► [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  19. Interleaved Filter • Choose candidate bandit at random • Make noisy comparisons (Bernoulli trial) against all other bandits simultaneously • Maintain mean and confidence interval for each pair of bandits being compared ► [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  20. Interleaved Filter • Choose candidate bandit at random • Make noisy comparisons (Bernoulli trial) against all other bandits simultaneously • Maintain mean and confidence interval for each pair of bandits being compared • …until another bandit is better • With confidence 1 – δ ► [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  21. Interleaved Filter • Choose candidate bandit at random • Make noisy comparisons (Bernoulli trial) against all other bandits simultaneously • Maintain mean and confidence interval for each pair of bandits being compared • …until another bandit is better • With confidence 1 – δ • Repeat process with new candidate • (Remove all empirically worse bandits) ► [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  22. Interleaved Filter • Choose candidate bandit at random • Make noisy comparisons (Bernoulli trial) against all other bandits simultaneously • Maintain mean and confidence interval for each pair of bandits being compared • …until another bandit is better • With confidence 1 – δ • Repeat process with new candidate • (Remove all empirically worse bandits) • Continue until 1 candidate left ► [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  23. Intuition • Simulate comparing candidate with all remaining bandits simultaneously [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  24. Intuition • Simulate comparing candidate with all remaining bandits simultaneously • Example: • P(A > B) = 0.85 • P(A > C) = 0.85 • P(B > C) = 0.51 • B is candidate • # comparisons between B vs C bounded by comparisons between B vs A! [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  25. Regret Analysis • Can model sequence of candidate bandits as a random walk. • Which will be the next candidate bandit? • O(Log K) rounds 1/3 1/3 1/3 [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  26. Regret Analysis • After each round, we remove a constant fraction of the remaining bandits. • O(K) total matches 4 matches 2 matches 0 matches [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  27. Regret Analysis • T – time horizon • K – # bandits / retrieval functions • ε – best vs 2nd best • Average regret RT / T → 0 • Information-theoretically optimal • Also need to prove correctness (see paper) [Yue, Broder, Kleinberg, Joachims, COLT 2009]

  28. Summary • Provably efficient online algorithm • (In a regret sense) • Also results for continuous (convex) setting

  29. Summary • Provably efficient online algorithm • (In a regret sense) • Also results for continuous (convex) setting • Requires comparison oracle • Reflects user preferences • Independence / Unbiased • Strong transitivity • Triangle Inequality

  30. Directions to Explore • Relaxing assumptions • E.g., strong transitivity & triangle inequality • Integrating context • Dealing with large K • Assume additional structure on for retrieval functions? • Other cost models • PAC setting (fixed budget, find the best possible) • Dynamic or changing user interests / environment

  31. Improving Comparison Oracle

  32. Improving Comparison Oracle • Dueling Bandits Problem • Interactive learning framework • Provably minimizes regret • Assumes idealized comparison oracle • Can we improve the comparison oracle? • Can we improve how we interpret results?

  33. Determining Statistical Significance • Each q, interleave A(q) and B(q), log clicks • t-Test • For each q, score: % clicks on A(q) • E.g., 3/4 = 0.75 • Sample mean score (e.g., 0.6) • Compute confidence (p value) • E.g., want p = 0.05 (i.e., 95% confidence) • More data, more confident

  34. Limitation • Example: query session with 2 clicks • One click at rank 1 (from A) • Later click at rank 4 (from B) • Normally would count this query session as a tie

  35. Limitation • Example: query session with 2 clicks • One click at rank 1 (from A) • Later click at rank 4 (from B) • Normally would count this query session as a tie • But second click is probably more informative… • …so B should get more credit for this query

  36. Linear Model • Feature vector φ(q,c): • Weight of click is wTφ(q,c) [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  37. Example • wTφ(q,c) differentiates last clicks and other clicks [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  38. Example • wTφ(q,c) differentiates last clicks and other clicks • Interleave A vs B • 3 clicks per session • Last click 60% on result from A • Other 2 clicks random [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  39. Example • wTφ(q,c) differentiates last clicks and other clicks • Interleave A vs B • 3 clicks per session • Last click 60% on result from A • Other 2 clicks random • Conventional w = (1,1) – has significant variance • Only count last click w = (1,0) – minimizes variance [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  40. Scoring Query Sessions • Feature representation for query session: [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  41. Scoring Query Sessions • Feature representation for query session: • Weighted score for query: • Positive score favors A, negative favors B [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  42. Supervised Learning • Will optimize for z-Test: Inverse z-Test • Approximately equal t-Test for large samples • z-Score = mean / standard deviation (Assumes A > B) [Yue, Gao, Chapelle, Zhang, Joachims, SIGIR 2010]

  43. Experiment Setup • Data collection • Pool of retrieval functions • Hash users into partitions • Run interleaving of different pairs in parallel • Collected on arXiv.org • 2 pools of retrieval functions • Training Pool: (6 pairs) know A > B • New Pool: (12 pairs)

  44. Training Pool – Cross Validation

  45. Experimental Results • Inverse z-Test works well • Beats baseline on most of new interleaving pairs • Direction of tests all in agreement • In 6/12 pairs, for p=0.1, reduces sample size by 10% • In 4/12 pairs, achieves p=0.05, but not baseline • 400 to 650 queries per interleaving experiment • Weights hard to interpret (features correlated) • Largest weight: “1 if single click & rank > 1”

  46. Interactive Learning • Dueling Bandits Problem • System learns “on-the-fly”. • Maximize total user utility over time • Exploration / exploitation tradeoff

  47. Interactive Learning • Dueling Bandits Problem • System learns “on-the-fly”. • Maximize total user utility over time • Exploration / exploitation tradeoff • Interpreting Implicit Feedback • Supervised learning to learn better interpretation • How do we “close the loop”? • Simple yet practical model • Efficient & compatible with existing approaches

  48. Thank You! Slides, papers & software available at www.yisongyue.com

  49. Extra Slides

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