ABSTRACT
Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items. In addition, these methods do not know how confident they are in their recommendations. Trust-based recommendation methods assume the additional knowledge of a trust network among users and can better deal with cold start users, since users only need to be simply connected to the trust network. On the other hand, the sparsity of the user item ratings forces the trust-based approach to consider ratings of indirect neighbors that are only weakly trusted, which may decrease its precision. In order to find a good trade-off, we propose a random walk model combining the trust-based and the collaborative filtering approach for recommendation. The random walk model allows us to define and to measure the confidence of a recommendation. We performed an evaluation on the Epinions dataset and compared our model with existing trust-based and collaborative filtering methods.
Supplemental Material
- 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. Google ScholarDigital Library
- R. M. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD 2007. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1), 1998. Google ScholarDigital Library
- D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In KDD 2008. Google ScholarDigital Library
- J. Golbeck. Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland College Park, 2005. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood a multifaceted collaborative filtering model. In KDD 2008. Google ScholarDigital Library
- Levien and Aiken. Advogato's trust metric. online at http://advogato.org/trust-metric.html, 2002.Google Scholar
- H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM '08, 2008. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust-aware recommender systems. In ACM Recommender Systems Conference (RecSys), USA, 2007. Google ScholarDigital Library
- S. Milgram. The small world problem. Psychology Today, 2, 1967.Google Scholar
- J. O'Donovan and B. Smyth. Trust in recommender systems. In 10th international conference on Intelligent user interfaces, USA, 2005. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD 2002. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW 2001. Google ScholarDigital Library
- S. Wasserman and K. Faust. Social Network Analysis. Cambridge Univ. Press, 1994.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- C. N. Ziegler. Towards Decentralized Recommender Systems. PhD thesis, University of Freiburg, 2005.Google Scholar
Index Terms
- TrustWalker: a random walk model for combining trust-based and item-based recommendation
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