trustwalker: a random walk model for combining trust-based and item-based recommendation mohsen...

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

Mohsen Jamali & Martin Ester

Simon Fraser University, Vancouver, Canada

Mohsen Jamali. TrustWalker: A Random Walk Model for Recommendation

Outline

• Introduction• TrustWalker

– Single Random Walk– Recommendation– Matrix Notation

• Properties of TrustWalker– Confidence, Special Extreme Cases

• Experiments• Conclusion and Future Work

2


Introduction - Recommendation

• 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

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

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

5


TrustWalker - Motivation

• Issues in Trust-based Recommendation– Noisy data in far

distances– Low probability of

Finding rater at close distances

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

7


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

8


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.

9


Item Similarities in TrustWalker

• Item Similarities

• Probability of having high correlation for pairs of items with few users in common is high.

10


Stopping Probability in TrustWalker

• Φu,i,k

– Similarity of items rated by u and target item i.– The step of random walk

11


Recommendation in TrustWalker

• Prediction = Expected value of rating returned by random walk.

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

13


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 [5][10].

• Confidence– How confident is the prediction

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.

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 Metrics

– RMSE– Coverage– Precision = 1- RMSE/4

16


Comparison Partner

• Tidal Trust [5]• Mole Trust [10]• CF Pearson• Random Walk 6,1• Item-based CF• TrustWalker0 [-pure]• TrustWalker [-pure]

17


Experiments – Cold Start Users

18


Experiment- All users

19


Experiments - Confidence

• More confident Predictions have lower error

20


Conclusion

• 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

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 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

• [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

• [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.

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trustwalker: a random walk model for combining trust-based and item-based recommendation mohsen...

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