Abstract
Recommendation on signed social rating networks is studied through an innovative approach. Bayesian probabilistic modeling is used to postulate a realistic generative process, wherein user and item interactions are explained by latent factors, whose relevance varies within the underlying network organization into user communities and item groups. Approximate posterior inference captures distrust propagation and drives Gibbs sampling to allow rating and (dis)trust prediction for recommendation along with the unsupervised exploratory analysis of network organization. Comparative experiments reveal the superiority of our approach in rating and link prediction on Epinions and Ciao, besides community quality and recommendation sensitivity to network organization.
Supplemental Material
Available for Download
Supplemental movie, appendix, image and software files for, Model-Based Collaborative Personalized Recommendation on Signed Social Rating Networks
- D. Agarwal and B.-C. Chen. 2009. Regression-based latent factor models. In Proc. of ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. 19--28. Google Scholar
Digital Library
- E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. 2008. Mixed membership stochastic blockmodels. Journal of Machine Learning Research 9 (2008), 1981--2014. Google Scholar
Digital Library
- L. Backstrom and J. Leskovec. 2011. Supervised random walks: Predicting and recommending links in social networks. In Proc. of ACM Int. Conf. on Web Search and Data Mining. 635--644. Google Scholar
Digital Library
- C. M. Bishop. 2013. Model-based machine learning. Philosophical Transactions of the Royal Society A (2013), 371:20120222.Google Scholar
- C. M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer. Google Scholar
Digital Library
- D. Blei. 2014. Build, compute, critique, repeat: Data analysis with latent variable models. Annual Review of Statistics and Its Application 1 (2014), 203--232.Google Scholar
- G. Costa, G. Manco, and R. Ortale. 2014. A generative Bayesian model for item and user recommendation in social rating networks with trust relationships. In Proc. of European Conference on Principles of Data Mining and Knowledge Discovery. 258--273.Google Scholar
- G. Costa and R. Ortale. 2012. A Bayesian hierarchical approach for exploratory analysis of communities and roles in social networks. In Proc. of the IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining. 194--201. Google Scholar
Digital Library
- G. Costa and R. Ortale. 2013. Probabilistic analysis of communities and inner roles in networks: Bayesian generative models and approximate inference. Social Network Analysis and Mining 3, 4 (2013), 1015--1038.Google Scholar
Cross Ref
- G. Costa and R. Ortale. 2014. A unified generative Bayesian model for community discovery and role assignment based upon latent interaction factors. In Proc. of the IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining. 93--100.Google Scholar
- M. DeGroot. 1970. Optimal Statistical Decisions. McGraw-Hill.Google Scholar
- J. Delporte, A. Karatzoglou, T. Matuszczyk, and S. Canu. 2013. Socially enabled preference learning from implicit feedback data. In Proc. of European Conf. on Machine Learning and Knowledge Discovery in Databases. 145--160.Google Scholar
- T. DuBois, J. Golbeck, J. Kleint, and A. Srinivasan. 2009. Improving recommendation accuracy by clustering social networks with trust. In Proc. of RecSys Workshop on Recommender Systems and the Social Web.Google Scholar
- R. Forsati, M. Mahdavi, M. Shamsfard, and M. Sarwat. 2014. Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Transactions on Information Systems 32, 4 (2014), 17:1--17:38. Google Scholar
Digital Library
- T. George and S. Merugu. 2005. A scalable collaborative filtering framework based on co-clustering. In Proc. of Int. Conf. on Data Mining. 625--628. Google Scholar
Digital Library
- N. Z. Gong, A. Talwalkar, L. Mackey, L. Huang, E. C. R. Shin, E. Stefanov, E. Shi, and D. Song. 2014. Joint link prediction and attribute inference using a social-attribute network. ACM Transactions on Intelligent Systems and Technology 5, 2 (2014), 27:1--27:20. Google Scholar
Digital Library
- R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. 2004. Propagation of trust and distrust. In Proc. of ACM Int. Conf. on World Wide Web. 403--412. Google Scholar
Digital Library
- I. Guy and D. Carmel. 2011. Social recommender systems. In Proc. of Int. Conf. Companion on World Wide Web. 283--284. Google Scholar
Digital Library
- I. Guy, L. Chen, and M. X. Zhou. 2013. Introduction to the special section on social recommender systems. ACM Transactions on Intelligent Systems and Technology 4, 1 (2013), 7:1--7:2. Google Scholar
Digital Library
- G. Heinrich. 2008. Parameter Estimation for Text Analysis. Technical Report. University of Leipzig. Available at http://www.arbylon.net/publications/text-est.pdf.Google Scholar
- T. Hofman, J. Puzicha, and M. I. Jordan. 1999. Learning from dyadic data. In Proc. of Advances in Neural Information Processing Systems. 466--472. Google Scholar
Digital Library
- M. Jamali and M. Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. of ACM Conf. on Recommender Systems. 135--142. Google Scholar
Digital Library
- M. Jamali, T. Huang, and M. Ester. 2011. A generalized stochastic block model for recommendation in social rating networks. In Proc. of ACM Conf. on Recommender Systems. 53--60. Google Scholar
Digital Library
- D. Koller and N. Friedman. 2009. Probabilistic Graphical Models. Principles and Techniques. MIT Press. Google Scholar
Digital Library
- J. Leskovec, D. Huttenlocher, and J. Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proc. of ACM Int. Conf. on World Wide Web. 641--650. Google Scholar
Digital Library
- D. Liben-Nowell and J. Kleinberg. 2007. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58, 7 (2007), 1019--1031. Google Scholar
Digital Library
- H. Ma, M. R. Lyu, and I. King. 2009. Learning to recommend with trust and distrust relationships. In Proc. of ACM Conf. on Recommender Systems. 189--196. Google Scholar
Digital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. 2008. Sorec: Social recommendation using probabilistic matrix factorization. In Proc. of ACM Conf. on Information and Knowledge Management. 931--940. Google Scholar
Digital Library
- H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. 2011a. Recommender systems with social regularization. In Proc. of ACM Int. Conf. on Web Search and Data Mining. 287--296. Google Scholar
Digital Library
- H. Ma, T. C. Zhou, M. R. Lyu, and I. King. 2011b. Improving recommender systems by incorporating social contextual information. ACM Transactions on Information Systems 29, 2 (2011), 9:1--9:23. Google Scholar
Digital Library
- X. Ma, H. Lu, and Z. Gan. 2014. Improving recommendation accuracy by combining trust communities and collaborative filtering. In Proc. of ACM Int. Conf. on Information and Knowledge Management. 1951--1954. Google Scholar
Digital Library
- L. Mackey, D. Weiss, and M. I. Jordan. 2010. Mixed membership matrix factorization. In Proc. of Int. Conf. on Machine Learning. 711--718.Google Scholar
Digital Library
- P. Massa and P. Avesani. 2007. Trust-aware recommender systems. In Proc. of ACM Conf. on Recommender Systems. 17--24. Google Scholar
Digital Library
- A. K. Menon and C. Elkan. 2011. Link prediction via matrix factorization. In Proc. of European Conference on Machine Learning. 437--452. Google Scholar
Digital Library
- K. T. Miller, T. L. Griffiths, and M. I. Jordan. 2009. Nonparametric latent feature models for link prediction. In Proc. of Advances in Neural Information Processing Systems. 1276--1284.Google Scholar
- M. C. Pham, Y. Cao, R. Klamma, and M. Jarke. 2011. A clustering approach for collaborative filtering recommendation using social network analysis. Journal of Universal Computer Science 17, 4 (2011), 583--604.Google Scholar
- G. Pitsilis, X. Zhang, and W. Wang. 2011. Clustering recommenders in collaborative filtering using explicit trust information. In Proc. of Trust Management V. 82--97.Google Scholar
- S. Purushotham, Y. Liu, and C.-C. J. Kuo. 2012. Collaborative topic regression with social matrix factorization for recommendation systems. In Proc. of Int. Conf. on Machine Learning. 759--766.Google Scholar
- R. Salakhutdinov and A. Mnih. 2008. Bayesian probabilistic matrix factorization using Markov chain monte carlo. In Proc. of Int. Conf. on Machine Learning. 880--887. Google Scholar
Digital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. 2000. Application of dimensionality reduction in recommender system -- a case study. In ACM WebKDD 2000 Workshop.Google Scholar
- Y. Shen and R. Jin. 2012. Learning personal+social latent factor model for social recommendation. In Proc. of ACM SIGKDD Int. Conf on Knowledge Discovery and Data Mining. 1303--1311. Google Scholar
Digital Library
- Y. Shi, M. Larson, and A. Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. Computer Surveys 47, 1 (2014), 3:1--3:45. Google Scholar
Digital Library
- L. Si, R. Jin, and C. Zhai. 2006. A study of mixture models for collaborative filtering. Information Retrieval 9, 3 (2006), 357--382. Google Scholar
Digital Library
- A. P. Singh and G. J. Gordon. 2010. A Bayesian matrix factorization model for relational data. In Proc. of Conf. on Uncertainty in Artificial Intelligence. 556--563.Google Scholar
- D. Song, D. A. Meyer, and D. Tao. 2015. Efficient latent link recommendation in signed networks. In Proc. of ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. 1105--1114. Google Scholar
Digital Library
- J. Tang, S. Chang, C. Aggarwal, and H. Liu. 2015. Negative link prediction in social media. In Proc. of ACM Int. Conf. on Web Search and Data Mining. 87--96. Google Scholar
Digital Library
- J. Tang, H. Gao, and H. Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In Proc. of ACM Int. Conf. on Web Search and Data Mining. 93--102. Google Scholar
Digital Library
- J. Tang, X. Hu, and H. Liu. 2013. Social recommendation: A review. Social Network Analysis and Mining 3, 4 (2013), 1113--1133.Google Scholar
Cross Ref
- P. Victor, C. Cornelis, M. De Cock, and A. M. Teredesai. 2011. Trust- and distrust-based recommendations for controversial reviews. IEEE Intelligent Systems 26, 1 (2011), 48--55. Google Scholar
Digital Library
- P. Victor, N. Verbiest, C. Cornelis, and M. D. Cock. 2013. Enhancing the trust-based recommendation process with explicit distrust. ACM Transactions on the Web 7, 2 (2013), 6:1--6:19. Google Scholar
Digital Library
- Y. Wu, X. Liu, M. Xie, M. Ester, and Q. Yang. 2016. CCCF: Improving collaborative filtering via scalable user-item co-clustering. In Proc. of Int. Conf. on Web Search and Data Mining. 73--82. Google Scholar
Digital Library
- B. Xu, J. Bu, C. Chen, and D. Cai. 2012. An exploration of improving collaborative recommender systems via user-item subgroups. In Proc. of Int. Conf. on World Wide Web. 21--30. Google Scholar
Digital Library
- B. Yang, W. K. Cheung, and J. Liu. 2007. Community mining from signed social networks. IEEE Transactions on Knowledge and Data Engineering 19, 10 (2007), 1333--1348. Google Scholar
Digital Library
- S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. 2011. Like like alike: Joint friendship and interest propagation in social networks. In Proc. of Int. Conf. on World Wide Web. 537--546. Google Scholar
Digital Library
- X. Yang, H. Steck, and Y. Liu. 2012. Circle-based recommendation in online social networks. In Proc. of ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. 1267--1275. Google Scholar
Digital Library
- G. Zhao, M. L. Lee, W. Hsu, W. Chen, and H. Hu. 2013. Community-based user recommendation in uni-directional social networks. In Proc. of ACM Int. Conf. on Information and Knowledge Management. 189--198. Google Scholar
Digital Library
- J. Zhu. 2012. Max-margin nonparametric latent feature models for link prediction. In Proc. of Int. Conf. on Machine Learning. 719--726.Google Scholar
Index Terms
Model-Based Collaborative Personalized Recommendation on Signed Social Rating Networks
Recommendations
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 ...
Improved Personalized Recommendation based on Causal Association Rule and Collaborative Filtering
There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper ...
Alternative Formulas for Rating Prediction Using Collaborative Filtering
ISMIS '09: Proceedings of the 18th International Symposium on Foundations of Intelligent SystemsThis paper proposes and evaluates several alternate design choices for common prediction metrics employed by neighborhood-based collaborative filtering approach. It first explores the role of different baseline user averages as the foundation of ...






Comments