Learning to recommend with trust and distrust relationships

ABSTRACT

With the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in the industry, traditional recommender systems suffer from several problems. First, the sparseness of the user-item matrix seriously affects the recommendation quality. Second, traditional recommender systems ignore the connections among users, which loses the opportunity to provide more accurate and personalized recommendations. In this paper, aiming at providing more realistic and accurate recommendations, we propose a factor analysis-based optimization framework to incorporate the user trust and distrust relationships into the recommender systems. The contributions of this paper are three-fold: (1) We elaborate how user distrust information can benefit the recommender systems. (2) In terms of the trust relations, distinct from previous trust-aware recommender systems which are based on some heuristics, we systematically interpret how to constrain the objective function with trust regularization. (3) The experimental results show that the distrust relations among users are as important as the trust relations. The complexity analysis shows our method scales linearly with the number of observations, while the empirical analysis on a large Epinions dataset proves that our approaches perform better than the state-of-the-art approaches.

REFERENCES

• 1: P. Bedi, H. Kaur, and S. Marwaha. Trust based recommender system for semantic web. In Proc. of IJCAI'07, pages 2677--2682, 2007.
• 2: R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proc. of KDD '07, pages 95--104, San Jose, California, USA, 2007.
• 3: J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI '98, 1998.
• 4: J. Canny. Collaborative filtering with privacy via factor analysis. In Proc. of SIGIR '02, pages 238--245, Tampere, Finland, 2002.
• 5: M. Deshpande and G. Karypis. Item-based top-n recommendation. ACM Transactions on Information Systems, 22(1):143--177, 2004.
• 6: R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. of WWW '04, pages 403--412, New York, NY, USA, 2004.
• 7: T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In Proc. of SIGIR '03, pages 259--266, Toronto, Canada, 2003.
• 8: T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1):89--115, 2004.
• 9: R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In Proc. of SIGIR '04, pages 337--344, Sheffield, United Kingdom, 2004.
• 10: A. Kohrs and B. Merialdo. Clustering for collaborative filtering applications. In Proceedings of CIMCA, 1999.
• 11: Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. of KDD '08, pages 426--434, Las Vegas, Nevada, USA, 2008.
• 12: J. Kunegis, A. Lommatzsch, and C. Bauckhage. The slashdot zoo: mining a social network with negative edges. In Proc. of WWW '09, pages 741--750, Madrid, Spain, 2009.
• 13: G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, pages 76--80, Jan/Feb 2003.
• 14: N. N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In Proc. of SIGIR '08, pages 83--90, Singapore, Singapore, 2008.
• 15: H. Ma, I. King, and M. R. Lyu. Effective missing data prediction for collaborative filtering. In Proc. of SIGIR '07, pages 39--46, Amsterdam, The Netherlands, 2007.
• 16: H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In Proc. of SIGIR '09, pages 203--210, Boston, MA, USA, 2009.
• 17: H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: Social recommendation using probabilistic matrix factorization. In Proc. of CIKM '08, pages 931--940, Napa Valley, USA, 2008.
• 18: B. Marlin and R. S. Zemel. The multiple multiplicative factor model for collaborative filtering. In Proc. of ICML '04, page 73, Banff, Alberta, Canada, 2004.
• 19: P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proceedings of CoopIS/DOA/ODBASE, pages 492--508, 2004.
• 20: P. Massa and P. Avesani. Trust-aware recommender systems. In Proc. of RecSys '07, pages 17--24, Minneapolis, MN, USA, 2007.
• 21: J. O'Donovan and B. Smyth. Trust in recommender systems. In Proc. of IUI '05, pages 167--174, San Diego, California, USA, 2005.
• 22: J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proc. of ICML '05, 2005.
• 23: P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of CSCW '94, 1994.
• 24: R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proc. of ICML '08, 2008.
• 25: R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, volume 20, 2008.
• 26: B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proc. of WWW '01, pages 285--295, Hong Kong, Hong Kong, 2001.
• 27: L. Si and R. Jin. Flexible mixture model for collaborative filtering. In Proc. of ICML '03, 2003.
• 28: N. Srebro and T. Jaakkola. Weighted low-rank approximations. In Proc. of ICML '03, pages 720--727, 2003.
• 29: W. Wei, K. T. Chan, I. King, and J. H.-M. Lee. Rate: A review of reviewers in a manuscript review process. In Proc of WI '08, pages 204--207, 2008.
• 30: Y. Zhang and J. Koren. Efficient bayesian hierarchical user modeling for recommendation system. In Proc. of SIGIR '07, pages 47--54, Amsterdam, The Netherlands, 2007.
• 31: Z. Zheng, H. Ma, M. R. Lyu, and I. King. WSRec: A collaborative filtering based web service recommender system. In Proc. of ICWS '09, pages 437--444, 2009.
• 32: D. Zhou, B. Scholkopf, and T. Hofmann. Semi-supervised learning on directed graphs. In Advances in Neural Information Processing Systems, volume 17, 2005.
• 33: T. C. Zhou, H. Ma, I. King, and M. R. Lyu. TagRec: Leveraging tagging wisdom for recommendation. In Proc. of IEEE International Symposium on Social Intelligence and Networking, 2009.
• 34: C.-N. Ziegler and G. Lausen. Propagation models for trust and distrust in social networks. Information Systems Frontiers, 7(4--5):337--358, 2005.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  J. Computer Applications
  J.4 SOCIAL AND BEHAVIORAL SCIENCES

General Terms:
Algorithms, Experimentation

Keywords:
distrust, matrix factorization, recommender systems, social network, trust