ABSTRACT
We propose an efficient recommendation algorithm, by incorporating the side information of users' trust and distrust social relationships into the learning process of a Joint Non-negative Matrix Factorization technique based on Signed Graphs, namely JNMF-SG. The key idea in this study is to generate clusters based on signed graphs, considering positive and negative weights for the trust and distrust relationships, respectively. Using a spectral clustering approach for signed graphs, the clusters are extracted on condition that users with positive connections should lie close, while users with negative ones should lie far. Then, we propose a Joint Non-negative Matrix factorization framework, by generating the final recommendations, using the user-item and user-cluster associations over the joint factorization. In our experiments with a dataset from a real-world social media platform, we show that we significantly increase the recommendation accuracy, compared to state-of-the-art methods that also consider the trust and distrust side information in matrix factorization.
- A. Anderson, R. Kumar, A. Tomkins, and S. Vassilvitskii. The dynamics of repeat consumption. In ACM International Conference on World Wide Web WWW'15, pages 419--430, 2014. Google ScholarDigital Library
- D. G. F. M. Bollen, M. P. Graus, and M. C. Willemsen. Remembering the stars?: effect of time on preference retrieval from memory. In ACM Conference on Recommender Systems, RecSys '12, pages 217--220, 2012. Google ScholarDigital Library
- K. Chiang, C. Hsieh, N. Natarajan, I. S. Dhillon, and A. Tewari. Prediction and clustering in signed networks: a local to global perspective. Journal of Machine Learning Research, 15(1):1177--1213, 2014. Google ScholarDigital Library
- R. Forsati, M. Mahdavi, M. Shamsfard, and M. Sarwat. Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans. Inf. Syst., 32(4):17:1--17:38, 2014. Google ScholarDigital Library
- R. V. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In ACM International Conference on World Wide Web, WWW '04, pages 403--412, 2004. Google ScholarDigital Library
- Y. Hou. Bounds for the least laplacian eigenvalue of a signed graph. Acta Mathematica Sinica, 21(4):955--960, 2005.Google ScholarCross Ref
- B. S. Kim, H. Kim, J. Lee, and J.-H. Lee. Improving a recommender system by collective matrix factorization with tag information. In Soft Computing and Intelligent Systems, SCIS '14, pages 980--984, 2014.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
- J. Kunegis, S. Schmidt, A. Lommatzsch, J. Lerner, E. W. D. Luca, and S. Albayrak. Spectral analysis of signed graphs for clustering, prediction and visualization. In SIAM International Conference on Data Mining, SDM '10, pages 559--570, 2010.Google ScholarCross Ref
- N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. In ACM SIGIR International Conference on Research and Development in Information Retrieval, SIGIR'10, pages 210--217, 2010. Google ScholarDigital Library
- D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788--791, 1999.Google ScholarCross Ref
- C. Liu, J. Liu, and Z. Jiang. A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Transactions Cybernetics, 44(12):2274--2287, 2014.Google ScholarCross Ref
- J. Liu, C. Wang, J. Gao, and J. Han. Multi-view clustering via joint nonnegative matrix factorization. In SIAM International Conference on Data Mining, SMD '13, pages 252--260, 2013.Google ScholarCross Ref
- N. N. Liu, L. He, and M. Zhao. Social temporal collaborative ranking for context aware movie recommendation. ACM Transactions on Intelligent Systems and Technology, 4(1):15:1--15:26, 2013. Google ScholarDigital Library
- D. Lo, D. Surian, K. Zhang, and E. Lim. Mining direct antagonistic communities in explicit trust networks. In ACM International Conference on Conference on Information and Knowledge Management, CIKM'11. Google ScholarDigital Library
- U. Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395--416, 2007. Google ScholarDigital Library
- H. Ma, M. R. Lyu, and I. King. Learning to recommend with trust and distrust relationships. In ACM Conference on Recommender Systems, RecSys '09, pages 189--196, 2009. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In ACM Conference on Information and Knowledge Management, CIKM '08, pages 931--940, 2008. Google ScholarDigital Library
- P. Matuszyk, J. a. Vinagre, M. Spiliopoulou, A. M. Jorge, and J. a. Gama. Forgetting methods for incremental matrix factorization in recommender systems. In ACM Symposium on Applied Computing, SAC '15, pages 947--953, 2015. Google ScholarDigital Library
- D. Rafailidis, A. Axenopoulos, J. Etzold, S. Manolopoulou, and P. Daras. Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging. ACM Transactions on Interactive Intelligent Systems, 3(4):26, 2014. Google ScholarDigital Library
- D. Rafailidis and P. Daras. The TFC model: Tensor factorization and tag clustering for item recommendation in social tagging systems. IEEE Transactions Systems, Man, and Cybernetics: Systems, 43(3):673--688, 2013.Google ScholarCross Ref
- D. Rafailidis and A. Nanopoulos. Modeling the dynamics of user preferences in coupled tensor factorization. In ACM Conference on Recommender Systems, RecSys'14, pages 321--324, 2014. Google ScholarDigital Library
- D. Rafailidis and A. Nanopoulos. Repeat consumption recommendation based on users preference dynamics and side information. In ACM International Conference on World Wide Web Companion, WWW'15 - Companion Volume, pages 99--100, 2015. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Conference on Neural Information Processing Systems, NIPS '07, pages 1257--1264, 2007.Google Scholar
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, WWW '01, pages 285--295, 2001. Google ScholarDigital Library
- Q. Song, J. Cheng, and H. Lu. Incremental matrix factorization via feature space re-learning for recommender system. In ACM Conference on Recommender Systems, RecSys '15, pages 277--280, 2015. Google ScholarDigital Library
- P. Victor, N. Verbiest, C. Cornelis, and M. D. Cock. Enhancing the trust-based recommendation process with explicit distrust. TWEB, 7(2):6, 2013. Google ScholarDigital Library
- H. Zhang, Z. Li, Y. Chen, X. Zhang, and S. Wang. Exploit latent dirichlet allocation for one-class collaborative filtering. In ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, pages 1991--1994, 2014. Google ScholarDigital Library
Index Terms
- Modeling trust and distrust information in recommender systems via joint matrix factorization with signed graphs
Recommendations
Learning to Rank with Trust and Distrust in Recommender Systems
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsThe sparsity of users' preferences can significantly degrade the quality of recommendations in the collaborative filtering strategy. To account for the fact that the selections of social friends and foes may improve the recommendation accuracy, we ...
Recommendations in Signed Social Networks
WWW '16: Proceedings of the 25th International Conference on World Wide WebRecommender systems play a crucial role in mitigating the information overload problem in social media by suggesting relevant information to users. The popularity of pervasively available social activities for social media users has encouraged a large ...
Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation
With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their ...
Comments