A study of mixture models for collaborative filtering

Information Retrieval

Volumn 9, Issue 3, 2006, Pages 357-382

  Jin, Rong ^a   Si, Luo ^b   Zhai, Chengxiang ^c  

^a Michigan State University   (United States)

^b Purdue University   (United States)

^c University of Illinois at Urbana Champaign   (United States)

Author keywords

Collaborative filtering; Graphical model; Probabilistic model

Indexed keywords

EID: 33744797084     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-006-4651-1     Document Type: Article

References (19)

1. Breese JS, Heckerman D and Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In the Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligence
2. Cohen W, Shapire R and Singer Y (1998) Learning to order things. In: Advances in Neural Processing Systems 10. MIT Press, Denver, CO, 1997
3. Connor M and Herlocker J (2001) Clustering items for collaborative filtering. In the Proceedings of SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA
4. Dempster AP, Laird NM and Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B39:1-38
5. Fisher D, Mil dram K, Hong J, Newman M and Vuduc R (2000) S WAMI: A framework for collaborative filtering algorithm development and evaluation. In the Proceedings of the 23rd Annual International Conference on Research and Development in Information Retrieval (SIGIR)
6. Freund Y, Iyer R, Shapire R and Singer Y (1998) An efficient boosting algorithm for combining preferences. In Proceedings of ICML 1998
7. Ha V and Haddawy P (1998) Toward case-based preference elicitation: Similarity measures on preference structures. In: Proceedings of UAI 1998
8. Herlocker JL, Konstan JA, Brochers A and Riedl J (1999) An algorithm framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
9. Hofmann T and Puzicha J (1999) Latent class models for collaborative filtering. In: Proceedings of International Joint Conference on Artificial Intelligence 1999
10. Hofmann T and Puzicha J (1998) Statistical models for co-occurrence data (Technical report). Artificial Intelligence Laboratory Memo 1625, M.I.T
11. Hofmann T (2003) Gaussian latent semantic models for collaborative filtering. In: Proceedings of the 26th Annual International ACM SIGIR Conference
12. Jin R, Si L and Zhai CX (2003) Preference-based graphical models for collaborative filtering. In: Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI)
13. Melville P, Mooney RJ and Nagarajan R (2002) Content-boosted collaborative filtering for improved recommendations. In the Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI)
14. Pennock DM, Horvitz E, Lawrence S and Giles CL (2000) Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In the Proceeding of the Sixteenth Conference on Uncertainty in Artificial Intelligence
15. Popescul A Ungar LH Pennock DM and Lawrence S (2001) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceeding of the Seventeenth Conference on Uncertainty in Artificial Intelligence
16. Resnick P, Iacovou N, Suchak M, Bergstrom P and Riedl J (1994) Grouplens: An open architecture for collaborative filtering of netnews. In Proceeding of the ACM 1994 Conference on Computer Supported Cooperative Work
17. Ross DA and Zemel RS (2002) Multiple-cause vector quantization. In NIPS-15: Advances in Neural Information Processing Systems 15
18. Si L and Jin R (2003) Product space mixture model for collaborative filtering. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML)
19. Ueda N and Nakano R (1998) Deterministic annealing EM algorithm. Neural Networks, 11(2):271-282