Improving One-Class Collaborative Filtering by incorporating rich user information

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2010, Pages 959-968

  Li, Yanen ^a   Hu, Jia ^b   Zhai, Chengxiang ^a   Chen, Ye ^c  

^a University of Illinois at Urbana Champaign ^*  (United States)

^b Texas AandM University   (United States)

^c MICROSOFT   (United States)

Author keywords

One Class Collaborative Filtering; Recommender systems; Rich user information

Indexed keywords

BASELINE METHODS; COLLABORATIVE FILTERING; DATA SETS; E-COMMERCE; IMPLICIT FEEDBACK; POSITIVE EXAMPLES; RECOMMENDER SYSTEMS; SEARCH QUERIES; USER INFORMATION;

KNOWLEDGE MANAGEMENT;

SIGNAL FILTERING AND PREDICTION;

EID: 78651310397     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1871437.1871559     Document Type: Conference Paper

References (23)

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng., 17(6):734-749, 2005.
2. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19-26, New York, NY, USA, 2006. ACM Press.
3. M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):66-72, 1997.
4. W. Chen, J.-C. Chu, J. Luan, H. Bai, Y. Wang, and E. Y. Chang. Collaborative filtering for orkut communities: discovery of user latent behavior. In WWW '09: Proceeding of the 18th International World, Wide Web Conference, pages 681-690. ACM, 2009.
5. Y. Chen and J. F. Canny. Probabilistic clustering of an item. U.S. Patent Application 12/694, 885, filed: Jan, 2010.
6. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, August 1999.
7. K. R. Gabriel and S. Zamir. Lower rank approximation of matrices by least squares with any choice of weights. Technometrics, 21(4):489-498, 1979.
8. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70, 1992.
9. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining (ICDM 2008), pages 263-272, 2008.
10. T. Joachims. Optimizing search engines using clickthrough data. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133-142, New York, NY, USA, 2002. ACM Press.
11. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426-434, New York, NY, USA, 2008. ACM.
12. Y. Koren. Collaborative filtering with temporal dynamics. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2009. ACM.
13. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, 2009.
14. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76-80, Jan/Feb 2003.
15. R. Pan and M. Scholz. Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 667-676. ACM, 2009.
16. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. M. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In IEEE International Conference on Data Mining (ICDM 2008), pages 502-511, 2008.
17. A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 437-444, 2001.
18. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 253-260, New York, NY, USA, 2002. ACM Press.
19. X. Shen, B. Tan, and C. Zhai. Implicit user modeling for personalized search. In CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, pages 824-831, New York, NY, USA, 2005. ACM.
20. V. Sindhwani, S. S. Bucak, J. Hu, and A. Mojsilovic. A family of non-negative matrix factorizations for one-class collaborative filtering. In ACM RecSys 2009, 2009.
21. N. Srebro and T. Jaakkola. Weighted low-rank approximations. In ICML '03: Proceedings of the 20th International Conference on Machine Learning, pages 720-727. AAAI Press, 2003.
22. B. Tan, X. Shen, and C. Zhai. Mining long-term search history to improve search accuracy. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 718-723, New York, NY, USA, 2006. ACM.
23. A. N. Tikhonov and V. Y. Arsenin. Solution of Ill-posed Problems. John Wiley & Sons, 1977.