Generalizing matrix factorization through flexible regression priors

RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Volumn , Issue , 2011, Pages 13-20

  Zhang, Liang ^a   Agarwal, Deepak ^a   Chen, Bee Chung ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

latent factor; matrix factorization; recommender systems; regression priors; tree models

Indexed keywords

COLD START; DATA SETS; GENERALIZED MATRIX; LATENT FACTOR; LOW RANK APPROXIMATIONS; MATRIX; MATRIX FACTORIZATION; MATRIX FACTORIZATIONS; MODEL FITTING; REGRESSION MODEL; REGRESSION MODELING; REGRESSION PRIORS; SMOOTH TRANSITIONS; STATE-OF-THE-ART PERFORMANCE; TREE MODELS; VARIABLE SELECTION; WARM START;

DECISION TREES; FACTORIZATION; FORESTRY; RECOMMENDER SYSTEMS; REGRESSION ANALYSIS;

MATRIX ALGEBRA;

ANALYSIS OF VARIANCE; DECISION THEORY; MATHEMATICAL MODELS; MATRICES; PERFORMANCE; REGRESSION ANALYSIS;

EID: 82555181744     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2043932.2043940     Document Type: Conference Paper

References (34)

1. KDD cup and workshop. 2007.
2. J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert. Low-rank matrix factorization with attributes. CoRR, abs/cs/0611124, 2006.
3. J. Abernethy, F. R. Bach, T. Evgeniou, and J.-P. Vert. A new approach to collaborative filtering:operator estimation with spectral regularization. JMLR, 2009.
4. D. Agarwal and B. Chen. Regression-based latent factor models. In KDD, 2009.
5. D. Agarwal and B. Chen. fLDA: matrix factorization through latent Dirichlet allocation. In WSDM, 2010.
6. D. Agarwal, B. Chen, P. Elango, N. Motgi, S. Park, R. Ramakrishnan, S. Roy, and J. Zachariah. Online models for content optimization. In NIPS, 2009.
7. M. Balabanovic and Y. Shoham. Fab: content-based, collaborative recommendation. Comm. of the ACM, 1997.
8. R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD, 2007.
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. J. S. Breese, D. Heckerman, and C. M. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, 1998.
11. L. Breiman. Classification and regression trees. Chapman & Hall/CRC, 1984.
12. L. Breiman. Random forests. Machine learning, 2001.
13. A. Z. Broder. Computational advertising and recommender systems. In RecSys, 2008.
14. H. Chipman, E. George, and R. McCulloch. BART: Bayesian additive regression trees. Annals of applied statistics, 2010.
15. M. Claypool and A. Gokhale, et al. Combining content-based and collaborative filters in an online newspaper. In RecSys Workshop, 1999.
16. 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.
17. J. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 2001.
18. A. E. Gelfand. Gibbs sampling. JASA, 1995.
19. N. Good and J. B. Schafer, et al. Combining collaborative filtering with personal agents for better recommendations. In AAAI, 1999.
20. P. Green. Penalized likelihood for general semi-parametric regression models. International Statistical Review/Revue Internationale de Statistique, 1987.
21. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2009.
22. N. Lawrence and R. Urtasun. Non-linear matrix factorization with gaussian processes. In ICML, 2009.
23. P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman & Hall/CRC, 1989.
24. P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In AAAI, 2002.
25. S.-T. Park and D. Pennock, et al. Naïve filterbots for robust cold-start recommendations. In KDD, 2006.
26. J. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, 2005.
27. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML, 2008.
28. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.
29. A. I. Schein, L. K. Saul, and L. H. Ungar. A generalized linear model for principal component analysis of binary data. In AISTATS, 2003.
30. N. Srebro and T. Jaakkola. Weighted low-rank approximations. In ICML, 2003.
31. N. Srebro, J. Rennie, and T. Jaakkola. Maximum margin matrix factorizations. In NIPS, 2005.
32. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In WWW, 2009.
33. R. Tibshirani. Regression shrinkage and selection via the lasso. J. of the Royal Stat. Soc. B, 1996.
34. Y. Zhang and J. Koren. Efficient bayesian hierarchical user modeling for recommendation system. In SIGIR, 2007.