Active Learning for Preferences Elicitation in Recommender Systems

1. Active Learning for Preferences Elicitation in Recommender Systems Lior Rokach Department of Information System Engineering :

2. Agenda • Background - Active Learning and Recommender Systems • Proposed Method • Experimental Procedure • Results and Discussion • Conclusions and Future Work

3. Recommender Systems • Users are overloaded by options to consider before making a decision • such as item to purchase • Recommender systems aim at supporting the user in the processes of • decision-making • planning • purchasing

4. Collaborative Filtering • Maintain users’ ratings of a variety of items. • For a given user: • Find other similar users whose ratings strongly correlate with the current user • Recommend items rated highly by these similar users, but not rated by the current user. • Almost all existing commercial recommenders use this approach (e.g. Amazon).

5. Collaborative Filtering

6. Active Learning • Traditional supervised learning algorithms • passively accept labeled training data and induce prediction model • Active learning • useful when unlabeled data is abundant • labels are expensive • allows intelligent selection of which examples to label. Passive Learning Active Learning

7. Using Active Learning for Initial Preferences Elicitation • The cold start problem • very little is known about the preferences of new users • Possible modus operandi • Ask the user to rate a few items • Which items ? Active Learning

8. Using Active Learning for Initial Preferences Elicitation Active Learning Active Learning

9. Active Learning in Critique-Based Recommender Systems(Ricci and Nguyen, 2007) • A series of interaction cycles to • narrow down the user’s query • until the desired item is obtained

10. Integrating Active Learning in CF-based Recommender Systems • Active Learning (AL) in RecSys • accurately predicts items of interest to the user • while gaining information about her preferences. • In this lecture we focus on • Uncertainty Active Collaborative Filtering • Boutilier et al. (2003) • Rong and Luo (2004) • …

11. Our Contributions • Incorporate exploration and exploitation trade-off. • Work local – thinkglobal • Use the ratings of one user to contribute to other users • Introduce Cost-Sensitivity (Not going to talk about that) the value of information of new ratings the alternative utility for not presenting the best items VS

12. Agenda • Background - Active Learning and Recommender Systems • Proposed Method • Experimental Procedure • Results and Discussion • Conclusions and Future Work

13. Preliminaries • Binary rating: Like/Dislike – • Explicit • Implicit - Based on user actions such as: • Buy • Click the item for additional details • Provide a recommendation of top n items • User selects from this list • Ignore the fact she can browse the remaining items. • We use a simple item-to-item NN CF • similarity measure such as Pearson correlation.

14. Item-to-Item NN CF with Binary Ratings rui*can be used to approximate the probability that user u would like item i. Some use Jaccard coefficient instead

15. Probabilistic Approach • Employ rule of succession (Laplace correction) • find the conditional probability for positive response in the next presentation of item i to user u: where itemSim should be normalized such that:

16. Mathematical interlude: Rule of succession • The proportionp of positive response is treated as a uniformly distributed random variable • Some claim that p is not random, but uncertain • We assign a probability distribution to p to express uncertainty, not to attribute randomness • Let Xi,j indicator variable • equals 1 when user i positively responded to an item j with probability pjof success (0 otherwise) • has a Bernoulli distribution.

17. Mathematical interlude: Rule of succession – cont. • Suppose these Xs are conditionally independent given pjthus the likelihood is: • The conditional probability distribution of pjgiven the data Xi,j, i = 1, ..., n, is the multiplication of the "prior" (i.e., marginal) probability measure assigned to pj by the likelihood function (Bayes' theorem)

18. The posterior probability density function is This is a beta distribution with expected value Rule of succession implies the conditional probability for positive response in the next presentation of item j given pj, is just pj. Mathematical interlude: Rule of succession – cont.

19. The Benefit and Risk of a Top 1 Recommendation • A simple scenario: • Recommend the best (top 1) item from only two possible items The risk: The presented item (item1) is not selected by the user, but if item 2 was presented to the user it would have been chosen

20. Risk Reduction • Risk reduces as more ratings become available

21. Risk Reduction Calculation Estimate CurrentRisk rui Positive With probability P(u,i)=0.2 Negative with probability 1-P(u,i)=0.8 Rebuild Recommendation List assuming rui=1 Rebuild Recommendation List assuming rui=0 Estimate NewRisk Estimate NewRisk RiskReduction = CurrentRisk - NewRisk

22. Loss/Utility • If the net revenues of the items are known, the risk/benefit is easily converted into loss/utility.

23. The Benefit and Risk of Top 1 Recommendation • Extended scenario: • Recommending the best (top 1) item from n possible items As Before

24. The Benefit and Risk of Top 1 Recommendation • High number of items limits the use of this formula in practice • Fortunately easy to calculate tight lower and upper bounds exist (Prekopa and Gao, 2005)

25. The Benefit and Risk of Top n Recommendation

26. Cascaded risk reduction for top n • Assumptions: • User selects only one item (positive response) • User reviews the items according to the their order in the list Estimate CurrentRisk ru1 P(u,1) 1-P(u,1) ru2 Estimate NewRisk P(u,2) 1-P(u,2) ru3 Estimate NewRisk 1-P(u,3) P(u,3) . . . Estimate NewRisk

27. Multiple Users • When user u provides an additional rating, • not only the risk/benefit of user u evolves • but also the risk/benefit of other users (CollaborativeFiltering)

28. Goal Formulation • U – set of Users • I – set of Items • DRLj– default ranked list for user j. • For example the list which would be selected by CF according to r*ui. • A Ranked List is an ordered set of pairs

29. Goal Formulation – cont. • Find PRLu (ranked list to be presented to user u) such that: • wv – weight for user v • Frequent users should have larger weights. • Tkis used to control the exploration/exploitation tradeoff • We employ simulated annealing with a simple and common exponential schedule:

30. Switching from active to passive Risk reduction converges to zero as number of ratings tends to infinity. • When sufficient ratings are achieved, go from active to passive

31. Proposition 1: Who is affected? • When a new rating for item i by user u, is added to an item-to-item NN CF, • the recommendation list of user v≠u is revised iff user v has rated at least one item that has been rated by user u. • Proof: • Straightforward

32. Illustration of Proposition 1 Not affected X

33. Proposition 2: How many are affected? • Assumption: the provided ratings are scattered uniformly over the item-users matrix, • Expected proportion of users affected by adding a new rating is: • where • N is the total number of items • n is the mean number of ratings provided by a single user • Example • N=2,000,000, n=210  prop=2% • N=17,000, n=210  prop=91%

34. A Greedy Algorithm • Finding the optimal ranked list for user u is a computationally intractable problem • Approximated solution • Greedily select the items to be presented from the top k·l items of user u, • l is the number of items presented in a single page, • kis a small integer • Calculate the risk reduction for a sample of m users selected randomly from all potentially affected users • Approximate the actual reduction by simple scaling

35. Computational Complexity • Assuming hash map structures for: • Rated items for each user • Rating users for each item • DRL for each user Greedily select items in the list Benefit Risk reduction

36. Agenda • Background - Active Learning and Recommender Systems • Proposed Method • Experimental Procedure • Results and Discussion • Conclusions and Future Work

37. The Experiments’ Goals • Compare the proposed active learning algorithm to passive learning. • Evaluate • contribution of the global effect • Monte-Carlo procedure for selecting the affected users • scalability of the greedy algorithm • Our main evaluation criterion: • Precision

38. Data • We actively select items to be presented to the user and expect to obtain the user’s response to these items. • Available offline datasets (such as Netflix) are sparse and therefore cannot guarantee response to all items we present. • Three options: • Find several sub-matrices that are dense • Filter DRLs according to the items known to be rated by the user. • Work online.

39. Offline evaluation • Six mutually exclusive dense submatrices of 50 users over 50 movies were extracted from Netflix • Provided ratings where transform it into a binary scale (ratings above user’s average are considered positive). • In each iteration we randomly selected a user and simulate a request for obtaining a recommendation assuming l=5, k=5. • Initial probability estimation of items for all users is assumed to be uniform.

40. Agenda • Background - Active Learning and Recommender Systems • Proposed Method • Experimental Procedure • Results and Discussion • Conclusions and Future Work

41. Offline (Netflix) ResultsPassive vs. Active Both methods display a unimodal peak quadratic-like growth. Both converges to the same value. The positive effect of active learning is maximally observed around of 200 sessions with an improvement of 15%.

42. Offline (Netflix) ResultsPassive vs. Active

43. Offline (Netflix) ResultsThe effect of recommendation list size (k)

44. Offline (Netflix) ResultsThe effect of number of referred users (m)

45. How much time it really takes? • In a real application • 8,000,000 items • 1,000,000 users • l=10,k=10,m=200 • About 12 msec with IntelCore Duo CPU E7400 @ 2.80GHz. • l=10,k=5,m=200 • About 1.5 msec

46. Agenda • Background - Active Learning and Recommender Systems • Proposed Method • Experimental Procedure • Results and Discussion • Conclusions and Future Work

47. Conclusions • A new Uncertainty Active Collaborative Filtering method has been developed. • The new method takes into consideration the global effect. • The new method can improve objective and subjective performance.

48. Drawbacks • Like any Uncertainty-based AL reducing uncertainty may not always improve accuracy (Rubens et al., 2010) • A more intensive calculation than the passive CF.

49. Future Work • Evaluate on a large dataset (under investigation) • Extends the method to other CF algorithms and compare to other Active Learning CF (under investigation) • Evaluate the method on a large scale online system (Scheduled to 4/2010) • Extend the algorithm to a non-binary scale (5 stars) • Develop a batch mode algorithm • Develop a better sampling method for selection of the affected users • Consider other heuristics • Taking into consideration the temporal aspect (Netflix)

50. Thank You, Lior Rokach Email: liorrk@bgu.ac.il