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

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

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

Introduction – Trust-based RS 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 Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 4

Trust-based Recommendation 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 Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 5

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

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. TrustWalker: A Random Walk Model for Recommendation 7

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. TrustWalker: A Random Walk Model for Recommendation 8

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

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

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

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

Performing Random Walks 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 Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 13

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

Related Work 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. Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 15

Experiments 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 Metric s –RMSE –Coverage –Precision = 1- RMSE/4 Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 16

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

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

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

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

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 Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 21

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

References [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 [2] R. M. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD [3] S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1), [4] D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In KDD [5] J. Golbeck. Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland College Park, [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), Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 23

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

References [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, [14] M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD [15] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW [16] S. Wasserman and K. Faust. Social Network Analysis. Cambridge Univ. Press, [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, [18] C. N. Ziegler. Towards Decentralized Recommender Systems. PhD thesis, University of Freiburg, Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation 25

TrustWalker

R1 5 Continue? Yes

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