Hui Li
Dingming Wu
ABSTRACT
Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recent approaches use data from social networks to improve accuracy. However, most of the social-network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this paper, we propose two alternative models that incorporate the overlapping community regularization into the matrix factorization framework. Our empirical study on four real datasets shows that our approaches outperform the state-of-the-art algorithms in both traditional and social-network based recommender systems regarding both cold-start users and normal users.
Supplemental Material
- E. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. In NIPS, pages 33--40, 2008.Google Scholar
Digital Library
- D. J. Crandall, D. Cosley, D. P. Huttenlocher, J. M. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In KDD, pages 160--168, 2008. Google ScholarDigital Library
- M. Deshpande and G. Karypis. Item-based top-phN recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143--177, 2004. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., pages 5--53, 2004. Google ScholarDigital Library
- T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In SIGIR, pages 259--266, 2003. Google ScholarDigital Library
- M. Jamali and M. Ester. Trustwalker: a random walk model for combining trust-based and item-based recommendation. In KDD, pages 397--406, 2009. Google ScholarDigital Library
- M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In RecSys, pages 135--142, 2010. Google ScholarDigital Library
- M. Jamali, T. Huang, and M. Ester. A generalized stochastic block model for recommendation in social rating networks. In RecSys, pages 53--60, 2011. Google ScholarDigital Library
- R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In SIGIR, pages 337--344, 2004. Google ScholarDigital Library
- A. Kohrs and B. Mérialdo. Clustering for collaborative filtering applications. In CIMCA, 1999.Google Scholar
- Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009. Google ScholarDigital Library
- H. Li, D. Wu, and N. Mamoulis. A revisit to social network-based recommender systems. In SIGIR, pages 1239--1242, 2014. Google ScholarDigital Library
- F. Liu and H. J. Lee. Use of social network information to enhance collaborative filtering performance. Expert Syst. Appl., 37(7):4772--4778, 2010. Google ScholarDigital Library
- N. N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In SIGIR, pages 83--90, 2008. Google ScholarDigital Library
- H. Ma. An experimental study on implicit social recommendation. In SIGIR, pages 73--82, 2013. Google ScholarDigital Library
- H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In SIGIR, pages 203--210, 2009. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM, pages 931--940, 2008. Google ScholarDigital Library
- H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, pages 287--296, 2011. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust-aware recommender systems. In RecSys, pages 17--24, 2007. Google ScholarDigital Library
- G. Palla, I. Derényi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814--818, 2005.Google ScholarCross Ref
- F. Reid, A. F. McDaid, and N. J. Hurley. Percolation computation in complex networks. In ASONAM, pages 274--281, 2012. Google ScholarDigital Library
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In CSCW, pages 175--186, 1994. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2007.Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285--295, 2001. Google ScholarDigital Library
- Y. Shen and R. Jin. Learning personal+social latent factor model for social recommendation. In KDD, pages 1303--1311, 2012. Google ScholarDigital Library
- L. Si and R. Jin. Flexible mixture model for collaborative filtering. In ICML, pages 704--711, 2003.Google ScholarDigital Library
- S. Wasserman and K. Faust. Social Network Analysis. Cambridge Univ. Press, 1994.Google ScholarCross Ref
- J. Yang and J. Leskovec. Overlapping community detection at scale: a nonnegative matrix factorization approach. In WSDM, pages 587--596, 2013. Google ScholarDigital Library
- J. Yang, J. J. McAuley, and J. Leskovec. Community detection in networks with node attributes. In ICDM, pages 1151--1156, 2013.Google ScholarCross Ref
- X. Yang, H. Steck, and Y. Liu. Circle-based recommendation in online social networks. In KDD, pages 1267--1275, 2012. Google ScholarDigital Library
- Y. Zhang and J. Koren. Efficient Bayesian hierarchical user modeling for recommendation system. In SIGIR, pages 47--54, 2007. Google ScholarDigital Library
Index Terms
- Overlapping Community Regularization for Rating Prediction in Social Recommender Systems
Recommendations
Recommender systems with social regularization
WSDM '11: Proceedings of the fourth ACM international conference on Web search and data miningAlthough Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social ...
Personalised rating prediction for new users using latent factor models
HT '11: Proceedings of the 22nd ACM conference on Hypertext and hypermediaIn recent years, personalised recommendations have gained importance in helping users deal with the abundance of information available online. Personalised recommendations are often based on rating predictions, and thus accurate rating prediction is ...
A social recommender system using item asymmetric correlation
Recommender systems have been one of the most prominent information filtering techniques during the past decade. However, they suffer from two major problems, which degrade the accuracy of suggestions: data sparsity and cold start. The popularity of ...
Comments