CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering

ABSTRACT

Collaborative Filtering (CF) is the most popular method for recommender systems. The principal idea of CF is that users might be interested in items that are favorited by similar users, and most of the existing CF methods measure users' preferences by their behaviours over all the items. However, users might have different interests over different topics, thus might share similar preferences with different groups of users over different sets of items. In this paper, we propose a novel and scalable method CCCF which improves the performance of CF methods via user-item co-clustering. CCCF first clusters users and items into several subgroups, where each subgroup includes a set of like-minded users and a set of items in which these users share their interests. Then, traditional CF methods can be easily applied to each subgroup, and the recommendation results from all the subgroups can be easily aggregated. Compared with previous works, CCCF has several advantages including scalability, flexibility, interpretability and extensibility. Experimental results on four real world data sets demonstrate that the proposed method significantly improves the performance of several state-of-the-art recommendation algorithms.

References

1. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 19--28. ACM, 2009.
2. E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. In Advances in Neural Information Processing Systems, pages 33--40, 2009.
3. A. Beutel, A. Ahmed, and A. J. Smola. Accams: Additive co-clustering to approximate matrices succinctly. In Proceedings of the 24th International Conference on World Wide Web, pages 119--129, 2015.
4. A. Beutel, K. Murray, C. Faloutsos, and A. J. Smola. Cobafi: collaborative bayesian filtering. In Proceedings of the 23rd international conference on World wide web, pages 97--108, 2014.
5. J. Foulds, L. Boyles, C. DuBois, P. Smyth, and M. Welling. Stochastic collapsed variational bayesian inference for latent dirichlet allocation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 446--454. ACM, 2013.
6. T. George and S. Merugu. A scalable collaborative filtering framework based on co-clustering. In ICDM'05. IEEE, 2005.
7. L. Hong, A. S. Doumith, and B. D. Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 557--566. ACM, 2013.
8. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, (8):30--37, 2009.
9. J. Lee, S. Bengio, S. Kim, G. Lebanon, and Y. Singer. Local collaborative ranking. In Proceedings of the 23rd international conference on World wide web, pages 85--96, 2014.
10. J. Lee, S. Kim, G. Lebanon, and Y. Singer. Local low-rank matrix approximation. In Proceedings of The 30th International Conference on Machine Learning, pages 82--90, 2013.
11. L. W. Mackey, M. I. Jordan, and A. Talwalkar. Divide-and-conquer matrix factorization. In Advances in Neural Information Processing Systems, pages 1134--1142, 2011.
12. A. Mnih and R. Salakhutdinov. Probabilistic matrix factorization. In Advances in neural information processing systems, pages 1257--1264, 2007.
13. M. O'Connor and J. Herlocker. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR workshop on recommender systems, volume 128, 1999.
14. V. Y. Pan and Z. Q. Chen. The complexity of the matrix eigenproblem. In Proceedings of the Thirty-first Annual ACM Symposium on Theory of Computing, STOC '99, pages 507--516. ACM, 1999.
15. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pages 452--461, 2009.
16. F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems handbook. Springer, 2011.
17. 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, pages 285--295. ACM, 2001.
18. H. Shan and A. Banerjee. Bayesian co-clustering. In ICDM'08, pages 530--539. IEEE, 2008.
19. H. Steck. Training and testing of recommender systems on data missing not at random. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 713--722. ACM, 2010.
20. P. Wang, C. Domeniconi, and K. B. Laskey. Latent dirichlet bayesian co-clustering. In Machine Learning and Knowledge Discovery in Databases, pages 522--537. Springer, 2009.
21. P. Wang, K. B. Laskey, C. Domeniconi, and M. I. Jordan. Nonparametric bayesian co-clustering ensembles. In SDM. SIAM, 2011.
22. J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI, pages 2764--2770, 2011.
23. J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. In Proceedings of the 29th International Conference on Machine Learning, pages 9--16, 2012.
24. J. Weston, R. J. Weiss, and H. Yee. Nonlinear latent factorization by embedding multiple user interests. In Proceedings of the 7th ACM conference on Recommender systems, pages 65--68. ACM, 2013.
25. B. Xu, J. Bu, C. Chen, and D. Cai. An exploration of improving collaborative recommender systems via user-item subgroups. In Proceedings of the 21st International Conference on World Wide Web, pages 21--30. ACM, 2012.
26. G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 114--121. ACM, 2005.
27. J. Yang and J. Leskovec. Community-affiliation graph model for overlapping network community detection. In ICDM'12, pages 1170--1175. IEEE, 2012.
28. J. Yang and J. Leskovec. Overlapping community detection at scale: A nonnegative matrix factorization approach. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pages 587--596. ACM, 2013.
29. J. Yang, J. McAuley, and J. Leskovec. Community detection in networks with node attributes. In ICDM'13, pages 1151--1156. IEEE, 2013.
30. S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 295--304. ACM, 2011.
31. Y. Zhang, M. Zhang, Y. Liu, and S. Ma. Improve collaborative filtering through bordered block diagonal form matrices. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 313--322. ACM, 2013.
32. Y. Zhang, M. Zhang, Y. Liu, S. Ma, and S. Feng. Localized matrix factorization for recommendation based on matrix block diagonal forms. In Proceedings of the 22Nd International Conference on World Wide Web, pages 1511--1520. ACM, 2013.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction