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TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation

TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation. Mohsen Jamali & Martin Ester Simon Fraser University, Vancouver, Canada. Introduction TrustWalker Single Random Walk Recommendation Matrix Notation Properties of TrustWalker

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TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation

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  1. TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation Mohsen Jamali & Martin Ester Simon Fraser University, Vancouver, Canada

  2. Introduction • TrustWalker • Single Random Walk • Recommendation • Matrix Notation • Properties of TrustWalker • Confidence, Special Extreme Cases • Experiments • Conclusion and Future Work Outline Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  3. Need For Recommenders • Problem Definition: • Given user u and target item i • Predict the rating ru,i • Collaborative Filtering • Considers Users with Similar Rating Patterns • Aggregates the ratings of Similar Users Introduction - Recommendation Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  4. Issues with CF • Requires Enough Ratings (Cold Start Users) • Vulnerable to Attack Profiles • Social Networks Emerged Recently • Independent source of information • Motivations of Trust-based RS • Social Influence: users adopt the behavior of their friends Introduction – Trust-based RS Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  5. Explores the trust network to find Raters. • Aggregate the ratings from raters for prediction. • Different weights for users • [5][10][8][18] • Advantages: • Improving the coverage • Attack resistance Trust-based Recommendation Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  6. Issues in Trust-based Recommendation • Noisy data in far distances • Low probability of Finding rater at close distances TrustWalker - Motivation Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  7. How Far to Go into Network? • Tradeoff between Precision and Recall • Trusted friends on similar items • Far neighbors on the exact target item TrustWalker - Motivation Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  8. 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 TrustWalker Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  9. 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. Single Random Walk Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  10. Item Similarities • Probability of having high correlation for pairs of items with few users in common is high. Item Similarities in TrustWalker Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  11. Φu,i,k • Similarity of items rated by u and target item i. • The step of random walk Stopping Probability in TrustWalker Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  12. Prediction = Expected value of rating returned by random walk. Recommendation in TrustWalker Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  13. Matrix Notation for TrustWalker • Expensive • We perform actual random walks • Result of a Single Random Walk is not precise • We perform several random walks • Prediction = Average of results • The variance of results of different random walk converges Performing Random Walks Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  14. 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 [5][10]. • Confidence • How confident is the prediction Properties of TrustWalker Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  15. Tidal Trust [5] • BFS to find raters at the closest distance • Mole Trust [10] • BFS to find rater up to depth max-depth • aggregate the ratings according to the trust values of the rater and the source user • Item-based CF [15] • Aggregate the ratings of source users on similar items weighted by their similarities. Related Work Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  16. Epinions.com Data Set • 49K users, 24K cold start users ( users with less than 5 ratings) • 104K items, 575K ratings, 508K trust expressions • Binary trust, ratings in [1,5] • Leave-one-out method • Evaluation Metrics • RMSE • Coverage • Precision = 1- RMSE/4 Experiments Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  17. Tidal Trust [5] • Mole Trust [10] • CF Pearson • Random Walk 6,1 • Item-based CF • TrustWalker0 [-pure] • TrustWalker [-pure] Comparison Partner Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  18. Experiments – Cold Start Users Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  19. Experiment- All users Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  20. More confident Predictions have lower error Experiments - Confidence Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  21. Conclusion • Random Walk Method • Combines Trust-based and Item-based Recommendation. • Computes the confidence in Predictions • Includes existing recommenders in its special cases. • Future Directions • Top-N recommendation [RecSys’09] • Distributed Recommender • Context dependent trust Conclusion Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  22. Thank You Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  23. [1] R. Andersen, C. Borgs, J. Chayes, U. Feige, A. Flaxman, A. Kalai, V. Mirrokni, and M. Tennenholtz. Trust-based Recommendation systems: an axiomatic approach. In WWW 2008. • [2] R. M. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD 2007. • [3] S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1), 1998. • [4] D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In KDD 2008. • [5] J. Golbeck. Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland College Park, 2005. • [6] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative ¯ltering to weave an information tapestry. Communications of the ACM, 35(12), 1992. References Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  24. [7] Y. Koren. Factorization meets the neighborhood a multifaceted collaborative ¯ltering model. In KDD 2008. • [8] Levien and Aiken. Advogato's trust metric. online at http://advogato.org/trust-metric.html, 2002. • [9] H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM '08, 2008. • [10] P. Massa and P. Avesani. Trust-aware recommender systems. In ACM Recommender Systems Conference (RecSys), USA, 2007. • [11] S. Milgram. The small world problem. Psychology Today, 2, 1967. • [12] J. O'Donovan and B. Smyth. Trust in recommender systems. In 10th international conference on Intelligent user interfaces, USA, 2005. References Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  25. [13] A. Rettinger, M. Nickles, and V. Tresp. A statistical relational model for trust learning. In AAMAS '08: 7th international joint conference on Autonomous agents and multiagent systems, 2008. • [14] M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD 2002. • [15] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW 2001. • [16] S. Wasserman and K. Faust. Social Network Analysis. Cambridge Univ. Press, 1994. • [17] H. Yildirim and M. S. Krishnamoorthy. A random walk method for alleviating the sparsity problem in collaborative filtering. In ACM Conference on Recommender Systems (RecSys), Switzerland, 2008. • [18] C. N. Ziegler. Towards Decentralized Recommender Systems. PhD thesis, University of Freiburg, 2005. References Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

  26. ? TrustWalker

  27. 5 TrustWalker

  28. 4 Continue? R1 5 Yes ? TrustWalker

  29. R1 5 R2 4 Continue? Yes Continue? R3 5 Yes Continue? No 5 Prediction = 4.67 TrustWalker

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