Online evolutionary collaborative filtering

RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems

Volumn , Issue , 2010, Pages 95-102

  Liu, Nathan N ^a   Zhao, Min ^b   Xiang, Evan ^a   Yang, Qiang ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b NEC Laboratories China   (China)

Author keywords

Collaborative filtering; Latent class model; Ranking

Indexed keywords

COLLABORATIVE FILTERING; COLLABORATIVE FILTERING ALGORITHMS; HISTORICAL DATA; INCREMENTAL ALGORITHM; LATENT CLASS MODEL; RANKING; REAL-WORLD APPLICATION; REAL-WORLD DATASETS; RECOMMENDATION ALGORITHMS; TEMPORAL INFORMATION; USER INTERESTS; USER'S INTEREST;

SIGNAL FILTERING AND PREDICTION;

EVOLUTIONARY ALGORITHMS;

EID: 78649960868     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1864708.1864729     Document Type: Conference Paper

References (21)

1. J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert. A new approach to collaborative filtering: Operator estimation with spectral regularization. Journal of Machine Learning Research, 10:803-826, 2009.
2. J. Bennett, C. Elkan, B. Liu, P. Smyth, and D. Tikk. Kdd cup and workshop 2007. SIGKDD Explorations, 9(2):51-52, 2007.
3. Y. Chi, X. Song, D. Zhou, K. Hino, and B. Tseng. Evolutionary spectral clustering by incorporating temporal smoothness. In KDD, pages 153-162, 2007.
4. A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 271-280, New York, NY, USA, 2007. ACM.
5. Y. Ding and X. Li. Time weight collaborative filtering. In Proc. of CIKM '05, pages 485-492, New York, NY, USA, 2005. ACM.
6. W. Fan. Systematic data selection to mine concept-drifting data streams. In KDD, pages 128-137, 2004.
7. J. Gao, W. Fan, J. Han, and P. S. Yu. A general framework for mining concept-drifting data streams with skewed distributions. In SDM, 2007.
8. K. Y. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133-151, 2001.
9. J. Herlocker, J. A. Konstan, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information retrieval, 5(4):287-310, 2002.
10. T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1):89-115, 2004.
11. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proc. of ICDM'08, pages 263-272, Washington, DC, USA, 2008. IEEE Computer Society.
12. R. Jin, L. Si, C. Zhai, and J. Callan. Collaborative filtering with decoupled models for preferences and ratings. In Proceedings of CIKM 2003, pages 309-106, 2003.
13. Y. Koren. Collaborative filtering with temporal dynamics. In Proc. of SIGKDD 2009, 2009.
14. N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. In Proc. of SIGIR 2009, 2009.
15. M. W. Mahoney, M. Maggioni, and P. Drineas. Tensor-cur decompositions for tensor-based data. In Proc. of KDD '06, pages 327-336, New York, NY, USA, 2006. ACM.
16. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. Proc. of ICDM'08, 0:502-511, 2008.
17. D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles. Collaborative filtering by personality diagnosis: A hybrid memory and model-based approach. In Proc. of UAI, pages 473-480, 2000.
18. J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proc. of ICML '05, pages 713-719, New York, NY, USA, 2005. ACM.
19. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285-295, 2001.
20. N. Srebro and T. Jaakkola. Weighted low-rank approximations. In ICML, pages 720-727, 2003.
21. J. Sun, D. Tao, and C. Faloutsos. Beyond streams and graphs: dynamic tensor analysis. In KDD, pages 374-383, 2006.