1. Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada UBC Data Mining Lab October 2010

2. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks2

3. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks3

4. Need For Recommenders Rapid Growth of Information Lots of Options for Users Input Data A set of users U={u 1, …, u N } A set of items I={i 1, …, i M } The rating matrix R=[r u,i ] NxM 4Mohsen Jamali, Recommendation in Social Networks

5. Predicting the rating on a target item for a given user ( i.e. Predicting Johns rating on Star Wars Movie ). Recommending a List of items to a given user ( i.e. Recommending a list of movies to John for watching ). movie1 ?? Recommender List of Top Movies ?? Recommender Movie 1Movie 2Movie 3 5Mohsen Jamali, Recommendation in Social Networks

6. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks6

7. Most Used and Well Known Approach for Recommendation Finds Users with Similar Interests to the target User Aggregating their opinions to make a recommendation. Often used for the prediction task 7Mohsen Jamali, Recommendation in Social Networks

8. TargetCustomer Aggregator Prediction 8Mohsen Jamali, Recommendation in Social Networks

9. Normally, there are a lot more users than items Collaborative Filtering doesnt scale well with users Item based Collaborative Filtering has been proposed in 2001 They showed that the quality of results are compatible in item based CF 9Mohsen Jamali, Recommendation in Social Networks

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11. Aggregator Prediction 11Mohsen Jamali, Recommendation in Social Networks

12. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks12

13. Social Networks Emerged Recently Independent source of information Motivation of SN-based RS Social Influence: users adopt the behavior of their friends Social Rating Network Social Network Trust Network Mohsen Jamali, Recommendation in Social Networks13

14. Cold Start users Very few ratings 50% of users Main target of SN recommenders Mohsen Jamali, Recommendation in Social Networks14 A Sample Social Rating Network

15. Classification of Recommenders Memory based Model based Memory based approaches for recommendation in social networks [Golbeck, 2005] [Massa et.al. 2007] [Jamali et.al. 2009] [Ziegler, 2005] Mohsen Jamali, Recommendation in Social Networks15

16. Explores the trust network to find Raters. Aggregate the ratings from raters for prediction. Different weights for users 16 Mohsen Jamali, Recommendation in Social Networks16

17. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks17

18. Cross Validation K-Fold Leave-one-out Root Mean Squared Error (RMSE) Mean Absolute Error (MAE) Mohsen Jamali, Recommendation in Social Networks18

19. Epinions – public domain Flixster Flixster.com is a social networking service for movie rating The crawled data set includes data from Nov 2005 – Nov 2009 Available at http://www.cs.sfu.ca/~sja25/personal/datasets/ Mohsen Jamali, Recommendation in Social Networks19

20. Mohsen Jamali, Recommendation in Social Networks20 General Statistics of Flixster and Epinions Flixster: 1M users, 47K items 150K users with at least one rating Items: movies 53% cold start Epinions: 71K users, 108K items Items: DVD Players, Printers, Books, Cameras,… 51% cold start

21. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks21

22. Issues in Trust-based Recommendation Noisy data in far distances Low probability of Finding rater at close distances 22 Mohsen Jamali, Recommendation in Social Networks22

23. How Far to Go into Network? Tradeoff between Precision and Recall Trusted friends on similar items Far neighbors on the exact target item 23 Mohsen Jamali, Recommendation in Social Networks23

24. TrustWalker Random Walk Model Combines Item-based Recommendation and Trust-based Recommendation Random Walk To find a rating on the exact target item or a similar item Prediction = returned rating 24 Mohsen Jamali, Recommendation in Social Networks24

25. Starts from Source user u 0. At step k, at node u: If u has rated I, return r u,i With Φ u,i,k, the random walk stops Randomly select item j rated by u and return r u,j. With 1- Φ u,i,k, continue the random walk to a direct neighbor of u. 25 Mohsen Jamali, Recommendation in Social Networks25

26. Item Similarities Φ u,i,k Similarity of items rated by u and target item i. The step of random walk 26Mohsen Jamali, Recommendation in Social Networks

27. Prediction = Expected value of rating returned by random walk. 27Mohsen Jamali, Recommendation in Social Networks

28. Special Cases of TrustWalker Φ u,i,k = 1 Random Walk Never Starts. Item-based Recommendation. Φ u,i,k = 0 Pure Trust-based Recommendation. Continues until finding the exact target item. Aggregates the ratings weighted by probability of reaching them. Existing methods approximate this. Confidence How confident is the prediction 28Mohsen Jamali, Recommendation in Social Networks

29. Evaluation method Leave-one-out Evaluation Metric s RMSE Coverage Precision = 1- RMSE/4 29Mohsen Jamali, Recommendation in Social Networks

30. Tidal Trust [Golbeck, 2005] Mole Trust [Massa, 2007] CF Pearson Random Walk 6,1 Item-based CF TrustWalker0 [-pure] TrustWalker [-pure] 30 Mohsen Jamali, Recommendation in Social Networks30

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33. More confident Predictions have lower error 33Mohsen Jamali, Recommendation in Social Networks

34. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks34

35. Model based approach Latent features for users Latent features for items Ratings are scaled to [0,1] g is logistic function Mohsen Jamali, Recommendation in Social Networks35 U and V have normal priors

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37. Issues with STE Feature vectors of neighbors should influence the feature vector of u not his ratings STE does not handle trust propagation Learning is based on observed ratings only. Mohsen Jamali, Recommendation in Social Networks37

38. Social Influence behavior of a user u is affected by his direct neighbors N u. Latent characteristics of a user depend on his neighbors. T u,v is the normalized trust value. Mohsen Jamali, Recommendation in Social Networks38

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44. Properties of SocialMF Trust Propagation User latent feature learning possible with existence of the social network No need to fully observed rating for learning Appropriate for cold start users Mohsen Jamali, Recommendation in Social Networks44

45. 5-fold cross validation Using RMSE for evaluation Comparison Partners Basic MF STE CF Model parameters SocialMF: STE: Mohsen Jamali, Recommendation in Social Networks45

46. Gain over STE: 6.2%. for K=5 and 5.7% for K=10 Mohsen Jamali, Recommendation in Social Networks46

47. SocialMF gain over STE (5%) is 3 times the STE gain over BasicMF (1.5%) Mohsen Jamali, Recommendation in Social Networks47

48. Lower error for Flixster Mohsen Jamali, Recommendation in Social Networks48 Epinions Flixster

49. Mohsen Jamali, Recommendation in Social Networks49 Sensitivity Analysis for Epinions

50. Mohsen Jamali, Recommendation in Social Networks50 Sensitivity Analysis for Flixster

51. Mohsen Jamali, Recommendation in Social Networks51 RMSE values on cold start users (K=5)

52. Mohsen Jamali, Recommendation in Social Networks52 RMSE values on cold start users (K=5)

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54. SocialMF: STE: SocialMF is faster by factor Mohsen Jamali, Recommendation in Social Networks54 N# of Users KLatent Feature Size Avg. ratings per user Avg. neighbors per user

55. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion Mohsen Jamali, Recommendation in Social Networks55

56. TrustWalker [KDD 2009] Memory-based Random walk approach SocialMF [RecSys 2010] Model based Matrix Factorization approach Other work Top-N Recommendation (RecSys 2009) Link Prediction (ACM TIST 2010) Mohsen Jamali, Recommendation in Social Networks56

57. Future Work Framework for Clustering, Rating and Link Prediction Explaining the recommendations Constructing the social network from observed data. Mohsen Jamali, Recommendation in Social Networks57

58. Thank you! 58Mohsen Jamali, Recommendation in Social Networks