fLDA: Matrix factorization through latent dirichlet allocation

WSDM 2010 - Proceedings of the 3rd ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2010, Pages 91-100

  Agarwal, Deepak ^a   Chen, Bee Chung ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Bayesian hierarchical model; Collaborative filtering; Graphical model; Latent factor model; Recommender systems; Supervised topic model

Indexed keywords

BAYESIAN HIERARCHICAL MODEL; COLLABORATIVE FILTERING; GRAPHICAL MODEL; LATENT FACTOR; RECOMMENDER SYSTEMS; TOPIC MODEL;

BAYESIAN NETWORKS; FACTORIZATION; GRAPHIC METHODS; HIERARCHICAL SYSTEMS; INFORMATION RETRIEVAL; LIGHT MEASUREMENT; MODELS; REGRESSION ANALYSIS; SIGNAL FILTERING AND PREDICTION;

WORLD WIDE WEB;

EID: 77950940282     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1718487.1718499     Document Type: Conference Paper

References (31)

1. KDD cup and workshop. 2007.
2. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In KDD, 2009.
3. D. Agarwal and B.-C. Chen, et al. Online models for content optimization. In NIPS, 2008.
4. M. Balabanovic and Y. Shoham. Fab: content-based, collaborative recommendation. Comm. of the ACM, 1997.
5. R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD, 2007.
6. R. M. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM, 2007.
7. D. Blei and J. McAuliffe. Supervised topic models. In NIPS, 2008.
8. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3, 2003.
9. J. Booth and J. Hobert. Maximizing generalized linear mixed model likelihoods with an automated monte carlo EM algorithm. J. R. Statist. Soc. B, 1999.
10. Y. Chen, D. Pavlov, and J. F. Canny. Large-scale behavioral targeting. In KDD, 2009.
11. M. Claypool and A. Gokhale, et al. Combining content-based and collaborative filters in an online newspaper. In Recommender Systems Workshop, 1999.
12. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc. B, 1977.
13. A. E. Gelfand. Gibbs sampling. JASA, 1995.
14. L. Getoor and B. Taskar. Introduction to Statistical Relational Learning. MIT Press, 2007.
15. N. Good and J. B. Schafer, et al. Combining collaborative filtering with personal agents for better recommendations. In AAAI, 1999.
16. T. L. Griffiths and M. Steyvers. Finding scientific topics. In Proc. of National Academy of Sciences, 2004.
17. X. Jin, Y. Zhou, and B. Mobasher. A maximum entropy web recommendation system: Combining collaborative and content features. In KDD, 2005.
18. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD, 2008.
19. N. Lawrence and R. Urtasun. Non-linear matrix factorization with gaussian processes. In ICML, 2009.
20. S.-T. Park and D. Pennock, et al. Naïve filterbots for robust cold-start recommendations. In KDD, 2006.
21. I. Porteous, E. Bart, and M. Welling. Multi-hdp: A non parametric bayesian model for tensor factorization. In AAAI, 2008.
22. J. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, 2005.
23. P. E. Rossi, G. Allenby, and R. P. McCulloch. Bayesian Statistics and Marketing. John Wiley, 2005.
24. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML'08, 2008.
25. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.
26. A. I. Schein and R. Popescul, et al. Methods and metrics for cold-start recommendations. In SIGIR, 2002.
27. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In KDD, 2008.
28. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In WWW, 2009.
29. Y. Wang and H. Bai, et al. Plda: Parallel latent dirichlet allocation for large-scale applications. In AAIM, 2009.
30. K. Yu, J. Lafferty, and S. Zhu. Large-scale collaborative prediction using a nonparametric random effects model. In ICML, 2009.
31. C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW, 2005.