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Recommendation in Social Networks

Recommendation in Social Networks. Mohsen Jamali , Martin Ester Simon Fraser University Vancouver, Canada. UBC Data Mining Lab October 2010. Outline. Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders TrustWalker SocialMF Conclusion. Outline.

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Recommendation in Social Networks

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

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

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

  4. Introduction • Need For Recommenders • Rapid Growth of Information • Lots of Options for Users • Input Data • A set of users U={u1, …, uN} • A set of items I={i1, …, iM} • The rating matrix R=[ru,i]NxM Mohsen Jamali, Recommendation in Social Networks

  5. Problem Definitions in RSs • Predicting the rating on a target item for a given user (i.e. Predicting John’s 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 Mohsen Jamali, Recommendation in Social Networks

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

  7. Collaborative Filtering • 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 Mohsen Jamali, Recommendation in Social Networks

  8. Target Customer Collaborative Filtering Aggregator Prediction Mohsen Jamali, Recommendation in Social Networks

  9. Item based Collaborative Filtering • Normally, there are a lot more users than items • Collaborative Filtering doesn’t 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 Mohsen Jamali, Recommendation in Social Networks

  10. Item-based Collaborative Filtering Mohsen Jamali, Recommendation in Social Networks

  11. Item-Item Collaborative Filtering Aggregator Prediction Mohsen Jamali, Recommendation in Social Networks

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

  13. Recommendation in Social Networks • 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 Networks

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

  15. Recommendation in Social Networks • 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 Networks

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

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

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

  19. Data Sets • 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 Networks

  20. Data Sets (cont.) • 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 Mohsen Jamali, Recommendation in Social Networks

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

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

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

  24. TrustWalker • 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 Mohsen Jamali, Recommendation in Social Networks

  25. Single Random Walk • Starts from Source user u0. • At step k, at node u: • If u has rated I, return ru,i • With Φu,i,k, the random walk stops • Randomly select item j rated by u and return ru,j . • With 1- Φu,i,k, continue the random walk to a direct neighbor of u. Mohsen Jamali, Recommendation in Social Networks

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

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

  28. Properties of TrustWalker • Special Cases of TrustWalker • Φu,i,k = 1 • Random Walk Never Starts. • Item-based Recommendation. • Φu,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 Mohsen Jamali, Recommendation in Social Networks

  29. Experimental Setups • Evaluation method • Leave-one-out • Evaluation Metrics • RMSE • Coverage • Precision = 1- RMSE/4 Mohsen Jamali, Recommendation in Social Networks

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

  31. Experiments – Cold Start Users Mohsen Jamali, Recommendation in Social Networks

  32. Experiment- All users Mohsen Jamali, Recommendation in Social Networks

  33. Experiments - Confidence • More confident Predictions have lower error Mohsen Jamali, Recommendation in Social Networks

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

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

  36. Social Trust Ensemble [2009] Mohsen Jamali, Recommendation in Social Networks

  37. Social Trust Ensemble (cont.) • 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 Networks

  38. The SocialMF Model • Social Influence  behavior of a user u is affected by his direct neighbors Nu. • Latent characteristics of a user depend on his neighbors. • Tu,v is the normalized trust value. Mohsen Jamali, Recommendation in Social Networks

  39. The SocialMF Model (cont.) Mohsen Jamali, Recommendation in Social Networks

  40. The SocialMF Model (cont.) Mohsen Jamali, Recommendation in Social Networks

  41. The SocialMF Model (cont.) Mohsen Jamali, Recommendation in Social Networks

  42. The SocialMF Model (cont.) Mohsen Jamali, Recommendation in Social Networks

  43. The SocialMF Model (cont.) Mohsen Jamali, Recommendation in Social Networks

  44. The SocialMF Model (cont.) • 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 Networks

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

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

  47. Results for Flixster • SocialMF gain over STE (5%) is 3 times the STE gain over BasicMF (1.5%) Mohsen Jamali, Recommendation in Social Networks

  48. Results (cont.) • Lower error for Flixster Epinions Flixster Mohsen Jamali, Recommendation in Social Networks

  49. Sensitivity Analysis on λT Sensitivity Analysis for Epinions Mohsen Jamali, Recommendation in Social Networks

  50. Sensitivity Analysis on λT Sensitivity Analysis for Flixster Mohsen Jamali, Recommendation in Social Networks

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