Using a Trust Network To Improve Top-N Recommendation

1. Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada Using a Trust Network To Improve Top-N Recommendation ACM RecSys 2009 Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

2. Outline • Introduction • Collaborative Filtering Approaches • User based CF • Item based CF • Trust based Approaches • Random Walk • Combined • Experiments • Conclusion Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

3. Introduction • Tasks of Recommender • Predicting an unknown rating • Recommending a list of items • Top-N Recommendation • Given a user u recommend a list of items Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

4. Collaborative Filtering for Top-N Recommendation • Item-based CF for top-N recommendation • [Deshpande 2004] • Binary rating matrix • Items: vectors in the user space. • Top similar items for each item purchased by the user • Most frequent items  top-N recommended items Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

5. Collaborative Filtering for Top-N Recommendation • User-based CF for top-N recommendation • Very few in the literature • [McLaughlin 2004] used as baseline • Find Similar users to user u according to Pearson correlation. • Aggregate the items rated by similar users to compute top-N recommendation • Most frequent items [McLaughlin 2004]. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

6. Using Trust Network for Top-N Recommendation • Issues with CF • Requires Enough Ratings • Cold Start Users • Cold Start Items • Social Networks Emerged Recently • Independent source of information • Motivations of Trust-based RS • Social Influence: users adopt the behavior of their friends Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

7. TrustWalker item i ? 4 4 2 3 5 1 4 3 2 2 2 3 3 2 3 2 2 4 5 3 3 3 2 3 4 2 3 4 2 1 4 1 3 4 4 4 4 2 5 1 3 1 5 4 5 4 4 3 user u 1 3 5 [Jamali 2009] Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

8. TrustWalker item i ? 4 4 2 3 4 3 1 5 2 3 3 2 2 2 3 2 2 4 5 3 3 3 2 4 3 3 2 4 2 1 4 4 1 4 4 4 3 2 5 1 1 3 5 4 5 4 4 3 user u Φ 1 3 5 [Jamali 2009] Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

9. TrustWalker item i ? 4 4 2 3 4 3 1 5 2 3 3 2 2 2 3 2 2 4 5 3 3 3 2 4 3 3 2 4 2 1 4 4 1 4 4 4 3 2 5 1 1 3 5 4 5 4 4 3 user u 1-Φ 1 3 5 [Jamali 2009] Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

10. Random Walk Approach • Extension of Trust Walker [Jamali 2009] • The random walk stops at a certain user v. • All items rated by v but not by u will be considered as recommended items • Several random walks. • Rank items by aggregate rating according to different random walks. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

11. Random Walk Approach (cont) • Single Random Walk • Start from the user u. • At each user v, with probability Φu,v,k stops and returns items rated by v. • With 1- Φu,v,k continue the random walk • We consider the current step k of random walk to avoid noisy data. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

12. Combined Approach • When a user u trusts another users v, it does not necessarily mean that they rate the same items. • Users who are similar according to Pearson correlation are more likely to have one more item in common. • Using Leave-one-out, CF may beat Random Walk based Approach by recommending the withheld item. • The list recommended by random walk approach may not contain the exact withheld item • Recommended items could still be related to the user Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

13. Combined Approach Recommended Movies Rated Movies Withheld Movie Images from www.imdb.com Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

14. Combined Approach • Find top k1 similar users and top k2 trusted users for u. K1 similar users K2 trusted users • Find the top-N items according to each set of users Merge the two sets of items to have one set of N items. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

15. Combined Approach (cont) • Finding Top k1 Similar Users • Similar to CF approaches • Finding Top k2 Trusted Users • Breadth First Search • Random Walk Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

16. Combined Approach (cont) • Aggregated rating for an item i CFu , TRu: items recommended using similar (trusted) users of u : aggregate rating according to k1 similar users : aggregate rating according to k2 trusted users Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

17. Experiments • We use Epinions.com Dataset • 49k users (24k cold start) • 104k items • 575k ratings, 508k trust relations. • N=100 • Evaluation Metric: Recall • L: number of queries. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

18. Comparison Partners • CF-User: User based Collaborative Filtering Approach. • CF-Item: Item based Collaborative Filtering Approach. • TrustWalkerList D2: RandomWalk approach with maxDepth=2. • TrustWalkerList D2-pure: The random Walk approach ignoring similarities. • TrustWalkerList D3: RandomWalk approach with maxDepth=3. • Trust-CF (k1=k2):The combined approach with k1 = k2. • Trust-CF (k1=70). k1 is fixed. The top k2 trusted users are computed using BFS. • Trust-CF-RW : same as Trust-CF (k1=70), but the top k2 trusted users are computed using a random walk approach. • Trust-CF Weighted. Combined approach with a fixed k1 and weighted merge of results of CF and trust-based approach. The trusted neighborhood is computed using BFS. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

19. Experimental Results Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

20. Experimental Results Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

21. Experimental Results Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

22. Conclusion • Addressing top-N Recommendation • Exploit both trust network and rating profiles • Experiments demonstrate that exploiting the social network improves the recommendations. • Future Work • More sophisticated combined models • Better suitable evaluation metrics • Distributed rating repositories Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

23. Thank you! Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

24. References • [Deshpande 2004] M. Deshpande and G. Karypis. Item based top-n recommendationalgorithms. ACM Transactions on Information Systems, 22:143–177, 2004. • [Golbeck 2005] J. Golbeck. Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland College Park, 2005. • [Goldberg 1992] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 1992. • [Jamali 2009] M. Jamali and M. Ester. Trustwalker: A random walk model for combining trust-based and item-based recommendation. In KDD’09: The 15th ACM SIGKDD conference on Knowledge Discovery andData Mining, 2009. • [Karypis 2001] G. Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM ’01: Proceedings of the tenth international conference on Information and knowledge management, pages 247–254, New York, NY, USA, 2001. • [Kim 2007] H.-N. Kim, A.-T. Ji, H.-J. Kim, and G.-S. Jo. Error-based collaborative filtering algorithm for top-n recommendation. In The Joint International Conferences on Asia-Pacific Web Conference and Web-Age Information Management (APWeb/WAIM), pages 594–605, Huang Shan, China, June 2007. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

25. References (cont) • [Kwon 2008] Y. Kwon. Improving top-n recommendation techniques using rating variance. In RecSys’08: Proceedings of the 2008 ACM conference on Recommender systems, pages 307–310, New York, NY, USA, 2008. • [Massa 2007] P. Massa and P. Avesani. Trust-aware recommender systems. In RecSys’07: ACM Recommender Systems Conference, USA, 2007. • [McLaughlin 2004] M. R. McLaughlin and J. L. Herlocker. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In SIGIR ’04: Proceedings of the 27th international ACM SIGIR conference on Information Retrieval, pages 329–336, New York, NY, USA, 2004. • [Richardson 2002] M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD’02: The 8th ACM SIGKDD conference on Knowledge Discovery andData Mining, 2002. • [Sarwar 2001] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW’01: 10th International World Wide Web Conference, 2001. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

26. Using Collaborative Filtering for Top-N Recommendation • User-based CF for top-N recommendation • Find top K similar users to u  Nu. • Aggregate the list of items rated by all vє Nu. • Similarity measure = Revised Pearson Correlation [4]

27. Using Collaborative Filtering for Top-N Recommendation • Item-based CF for top-N recommendation • Consider items rated highly by u I’u. • Find top K similar items to all items jєI’u. • Aggregate these items to compute top-N items • Similarity measure = Revised Pearson Correlation

28. Combined Approach (cont) • Finding k2 trusted users: • Breadth First Search • Random Walk • Perform several random walks to find k2 users. • Estimated rating for an item would be mean of ratings expressed by trusted friends on item i.

29. Experimental Results – Cold Start Users

30. Experimental Results – Cold Start Users

31. Experimental Results – Cold Start Users

32. Experimental Results • Mixture weights are not tuned