Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures

Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Volumn , Issue , 2010, Pages 563-568

  Porteous, Ian ^a   Asuncion, Arthur ^a   Welling, Max ^a  

^a Ringgold Standard Institution Informatics   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; COLLABORATIVE FILTERING; LEARNING SYSTEMS; MATRIX FACTORIZATION;

BAYESIAN MATRIX FACTORIZATIONS; DIRICHLET PROCESS; FILTERING INFORMATIONS; INFORMATION PROCESS; LATENT CLASS; MACHINE-LEARNING; MATRIX; MATRIX FACTORIZATIONS; MOVIE RATINGS; SIDE INFORMATION;

MATRIX ALGEBRA;

EID: 85071783077     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Agarwal, D., and Chen, B.-C. 2009. Regression-based latent factor models. In KDD'09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 19-28. New York, NY, USA: ACM.
2. Antoniak, C. 1974. Mixtures of Dirichlet processes with applications to bayesian nonparametric problems. The Annals of Statistics 2:1152-1174.
3. Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3:993-1022.
4. Deerwester, S.; Dumais, S.; Furnas, G.; Landauer, T.; and Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6):391-407.
5. Ferguson, T. 1973. A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1:209-230.
6. Ghahramani, Z.; Griffiths, T.; and Sollich, P. 2007. Bayesian nonparametric latent feature models. Bayesian Statistics 8.
7. Koren, Y. 2008. 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, 426-434. New York, NY, USA: ACM.
8. Lee, D., and Seung, H. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401:788-791.
9. Lim, Y., and Teh, Y. 2007. Variational Bayesian approach to movie rating prediction. In Proceedings of KDD Cup and Workshop.
10. Neal, R. 2000. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9:283-297.
11. Porteous, I.; Bart, E.; and Welling, M. 2008. Multi-hdp: A non parametric bayesian model for tensor factorization. In AAAI, 1487-1490.
12. Salakhutdinov, R., and Mnih, A. 2008. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML'08: Proceedings of the 25th international conference on Machine learning, 880-887. New York, NY, USA: ACM.
13. Stern, D.; Herbrich, R.; and Graepel, T. 2009. Matchbox: Large scale online bayesian recommendations. In 18th International World Wide Web Conference, 111-111.
14. Takács, G.; Pilászy, I.; Németh, B.; and Tikk, D. 2009. Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research 10:623-656.