Fast nonparametric matrix factorization for large-scale collaborative filtering

Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009

Volumn , Issue , 2009, Pages 211-218

  Yu, Kai ^a   Zhu, Shenghuo ^a   Lafferty, John ^b   Gong, Yihong ^a  

^a NEC LABORATORIES AMERICA   (United States)

^b Carnegie Mellon University ^*  (United States)

Author keywords

Collaborative filtering; Matrix factorization; Nonparametric models

Indexed keywords

ACCURATE PREDICTION; COLLABORATIVE FILTERING; COMPLEX DATA; DATA SIZE; DATA-DRIVEN; LARGE-SCALE PROBLEM; LATENT FACTOR; LOW-RANK MATRICES; MATRIX FACTORIZATIONS; NON-PARAMETRIC; NON-PARAMETRIC MODEL; ONLINE USERS; OPTIMIZATION ALGORITHMS; PREFERENCE LEARNING; PROBABILISTIC PRINCIPAL COMPONENT ANALYSIS; USER RATING;

COMPUTATIONAL EFFICIENCY; FACTORIZATION; INFORMATION RETRIEVAL; INFORMATION SERVICES; LIGHT MEASUREMENT; PRINCIPAL COMPONENT ANALYSIS; SIGNAL FILTERING AND PREDICTION; SINGULAR VALUE DECOMPOSITION;

LEARNING ALGORITHMS;

EID: 72449152230     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1571941.1571979     Document Type: Conference Paper

References (18)

1. J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert. Low-rank matrix factorization with attributes. Technical report, Ecole des Mines de Paris, 2006.
2. R. M. Bell, Y. Koren, and C. Volinsky. The BellKor solution to the Netflix prize. Technical report, AT&T Labs, 2007.
3. E. J. Candés and T. Tao. The power of convex relaxation: Near-optimal matrix completion. Submitted for publication, 2009.
4. D. DeCoste. Collaborative prediction using ensembles of maximum margin matrix factorization. In The 23rd International Conference on Machine Learning (ICML), 2006.
5. M. Kurucz, A. A. Benczur, and K. Csalogany. Methods for large scale SVD with missing values. In Proceedings of KDD Cup and Workshop, 2007.
6. Y. J. Lim and Y. W. Teh. Variational Bayesian approach to movie rating prediction. In Proceedings of KDD Cup and Workshop, 2007.
7. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.
8. J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In The 22nd International Conference on Machine Learning (ICML), 2005.
9. S. Roweis and Z. Ghahramani. A unifying review of linear Gaussian models. Neural Computaion, 11:305-345, 1999.
10. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In The 25th International Conference on Machine Learning (ICML), 2008.
11. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
12. N. Srebro, J. D. M. Rennie, and T. S. Jaakola. Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems 18 (NIPS), 2005.
13. G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. On the gravity recommendation system. In Proceedings of KDD Cup and Workshop, 2007.
14. M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statisitical Scoiety, B(61):611-622, 1999.
15. M. Wu. Collaborative filtering via ensembles of matrix factorizations. In Proceedings of KDD Cup and Workshop, 2007.
16. K. Yu, J. Lafferty, S. Zhu, and Y. Gong. Large-scale collaborative prediction using a nonparametric random effects model. In The 25th International Conference on Machine Learning (ICML), 2009.
17. K. Yu and V. Tresp. Learning to learn and collaborative filtering. In NIPS workshop on "Inductive Transfer: 10 Years Later", 2005.
18. Y. Zhang and J. Koren. Efficient Bayesian hierarchical user modeling for recommendation systems. In The 30th ACM SIGIR Conference, 2007.