Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

As an indispensable technique in the field of Information Filtering, Recommender System has been well studied and developed both in academia and in industry recently. However, most of current recommender systems suffer the following problems: (1) The large-scale and sparse data of the user-item matrix seriously affect the recommendation quality. As a result, most of the recommender systems cannot easily deal with users who have made very few ratings. (2) The traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real world recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework, which naturally fuses the users' tastes and their trusted friends' favors together. In this framework, we coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method performs better than the state-of-the-art approaches.

REFERENCES

• 1: Reid Andersen , Christian Borgs , Jennifer Chayes , Uriel Feige , Abraham Flaxman , Adam Kalai , Vahab Mirrokni , Moshe Tennenholtz, Trust-based recommendation systems: an axiomatic approach, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367525]
• 2: P. Bedi, H. Kaur, and S. Marwaha. Trust based recommender system for semantic web. In Proc. of IJCAI '07, pages 2677--2682, 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: John Canny, Collaborative filtering with privacy via factor analysis, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564419]
• 5: Mukund Deshpande , George Karypis, Item-based top-N recommendation algorithms, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.143-177, January 2004 [doi>10.1145/963770.963776]
• 6: Jonathan L. Herlocker , Joseph A. Konstan , Al Borchers , John Riedl, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.230-237, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312682]
• 7: Thomas Hofmann, Collaborative filtering via gaussian probabilistic latent semantic analysis, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860483]
• 8: Thomas Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.89-115, January 2004 [doi>10.1145/963770.963774]
• 9: Rong Jin , Joyce Y. Chai , Luo Si, An automatic weighting scheme for collaborative filtering, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009051]
• 10: Greg Linden , Brent Smith , Jeremy York, Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing, v.7 n.1, p.76-80, January 2003 [doi>10.1109/MIC.2003.1167344]
• 11: Nathan N. Liu , Qiang Yang, EigenRank: a ranking-oriented approach to collaborative filtering, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390351]
• 12: Hao Ma , Irwin King , Michael R. Lyu, Effective missing data prediction for collaborative filtering, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277751]
• 13: Hao Ma , Haixuan Yang , Michael R. Lyu , Irwin King, SoRec: social recommendation using probabilistic matrix factorization, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA [doi>10.1145/1458082.1458205]
• 14: P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proc. of CoopIS/DOA/ODBASE, pages 492--508, 2004.
• 15: Paolo Massa , Paolo Avesani, Trust-aware recommender systems, Proceedings of the 2007 ACM conference on Recommender systems, October 19-20, 2007, Minneapolis, MN, USA [doi>10.1145/1297231.1297235]
• 16: Jasson D. M. Rennie , Nathan Srebro, Fast maximum margin matrix factorization for collaborative prediction, Proceedings of the 22nd international conference on Machine learning, p.713-719, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102441]
• 17: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 18: Ruslan Salakhutdinov , Andriy Mnih, Bayesian probabilistic matrix factorization using Markov chain Monte Carlo, Proceedings of the 25th international conference on Machine learning, p.880-887, July 05-09, 2008, Helsinki, Finland [doi>10.1145/1390156.1390267]
• 19: R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, volume 20, 2008.
• 20: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]
• 21: L. Si and R. Jin. Flexible mixture model for collaborative filtering. In Proc. of ICML '03, 2003.
• 22: N. Srebro and T. Jaakkola. Weighted low-rank approximations. In Proc. of ICML '03, 2003.
• 23: Jun Wang , Arjen P. de Vries , Marcel J. T. Reinders, Unifying user-based and item-based collaborative filtering approaches by similarity fusion, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148257]
• 24: Yi Zhang , Jonathan Koren, Efficient bayesian hierarchical user modeling for recommendation system, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277752]

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:
matrix factorization, recommender systems, social network, social trust ensemble