Meng Jiang
Peng Cui
Qiang Yang
ABSTRACT
Exponential growth of information generated by online social networks demands effective recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social context has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users' motivation of social behaviors into social recommendation. In this paper, we investigate social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online item adoption and recommendation. Then we propose a novel probabilistic matrix factorization method to fuse them in latent spaces. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network datasets in China. The empirical result and analysis on these two large datasets demonstrate that our method significantly outperform the existing approaches.
- M. Balabanovic and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40:66--72, March 1997. Google Scholar
Digital Library
- A. Bandura. Social cognitive theory of mass communication. Media Psychology, 2001.Google Scholar
- L. S. Benjamin. Structural analysis of social behavior. Psychological Review, 1974.Google ScholarCross Ref
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, March 2003. Google ScholarDigital Library
- R. Bond and P. B. Smith. Culture and conformity: a meta-analysis of studies using asch's (1952b, 1956) line judgment task. Psychological Bulletin, 119: 111--137, January 1996.Google ScholarCross Ref
- Y. Chen and J. Canny. Recommending ephemeral items at web scale. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 1013--1022, 2011. Google ScholarDigital Library
- P. Cui, F. Wang, S. Liu, M. Ou, S. Yang, and L. Sun. Who should share what? item-level social influence prediction for users and posts ranking. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 185--194, 2011. Google ScholarDigital Library
- M. Harvey, M. J. Carman, I. Ruthven, and F. Crestani. Bayesian latent variable models for collaborative item rating prediction. In Proceedings of CIKM, pages 699--708, 2011. Google ScholarDigital Library
- M. Harvey, M. J. Carman, I. Ruthven, and F. Crestani. Bayesian latent variable models for collaborative item rating prediction. In Proceedings of CIKM, pages 699--708, 2011. Google ScholarDigital Library
- M. Harvey, M. J. Carman, I. Ruthven, and F. Crestani. Bayesian latent variable models for collaborative item rating prediction. In Proceedings of CIKM, pages 699--708, 2011. Google ScholarDigital Library
- M. Harvey, M. J. Carman, I. Ruthven, and F. Crestani. Bayesian latent variable models for collaborative item rating prediction. In Proceedings of CIKM, pages 699--708, 2011. Google ScholarDigital Library
- D. Kong, C. Ding, and H. Huang. Robust nonnegative matrix factorization using l21-norm. In Proceedings of CIKM, pages 673-682, 2011. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 426-434, 2008. Google ScholarDigital Library
- Y. Koren. Collaborative filtering with temporal dynamics. In Proceeding of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2009. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42:30--37, 2009. Google ScholarDigital Library
- J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. Lecture Notes in Computer Science, 3918:380--389, 2006. Google ScholarDigital Library
- L. Liu, J. Tang, J. Han, M. Jiang, and S. Yang. Mining topic-level influence in heterogeneous networks. In Proceedings of CIKM, pages 199--208, 2010. Google ScholarDigital Library
- N. N. Liu, M. Zhao, and Q. Yang. Probabilistic latent preference analysis for collaborative filtering. In Proceedings of CIKM, pages 759--766, 2009. Google ScholarDigital Library
- Q. Liu, E. Chen, H. Xiong, C. Ding, and J. Chen. Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics - Part B (TSMCB), 42(1):218--233, February 2012. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of CIKM, pages 931--940, 2008. Google ScholarDigital Library
- H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender system with social regularization. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM), pages 287--296, 2011. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys), 2007. Google ScholarDigital Library
- S. Moghaddam, M. Jamali, M. Ester, and J. Habibi. Feedbacktrust: using feedback effects in trust-based recommendation systems. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys), pages 269--272, 2009. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. Neural Information Processing Systems (NIPS), 2007.Google Scholar
- M. J. Salganik. Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311:854--856, 2006.Google ScholarCross Ref
- B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW), pages 285--295, 2001. Google ScholarDigital Library
- L. Si and R. Jin. Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model. In Proceedings of CIKM, pages 156--157, 2004. Google ScholarDigital Library
- M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61:611--622, 1999.Google ScholarCross Ref
- F. Wang, H. Tong, and C.-Y. Lin. Towards evolutionary nonnegative matrix factorization. In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI), pages 501--506, 2011.Google Scholar
- S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: joint friendship and interest propagation in social networks. In Proceedings of the 20th International Conference on World Wide Web (WWW), 2011. Google ScholarDigital Library
Index Terms
- Social contextual recommendation
Recommendations
Introduction to social recommendation
WWW '10: Proceedings of the 19th international conference on World wide webAs the exponential growth of information generated on the World Wide Web, Social Recommendation has emerged as one of the hot research topics recently. Social Recommendation forms a specific type of information filtering technique that attempts to ...
Recommendation via user's personality and social contextual
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementWith the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring ...
Social recommendation via deep neural network-based multi-task learning
AbstractRecent years are witnessing the rapid development of social recommendation to improve the performance of recommender system, especially for cold start problem. Many of the existing social recommendation techniques utilize matrix ...
Highlights- A social recommendation model (SoNeuMF) based on deep learning is proposed.
- ...
Comments