1 / 29

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

booker
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related