Non-linear matrix factorization with Gaussian processes

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 601-608

  Lawrence, Neil D ^a   Urtasun, Raquel ^b  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE METHODS; COLLABORATIVE FILTERING; DATA SETS; GAUSSIAN PROCESSES; LATENT VARIABLE MODELS; MATRIX FACTORIZATIONS; NON-LINEAR; STATE-OF-THE-ART PERFORMANCE; STOCHASTIC GRADIENT DESCENT;

APPROXIMATION THEORY; FACTORIZATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LIGHT MEASUREMENT; ROBOT LEARNING; STOCHASTIC MODELS;

MATRIX ALGEBRA;

EID: 71149119166     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Bell, R., & Koren, Y. (2007). Lessons from the Netflix prize challenge. SIGKDD Explorations 9, 75-79.
2. Bell, R. M., Koren, Y., & Volinsky, C. (2008). The BellKor 2008 solution to the netflix prize. Available from http://www.research.att.com/- volinsky/netflix/.
3. Bishop, C. M. (1999a). Bayesian PCA. Advances in Neural Information Processing Systems (pp. 482-388). Cambridge, MA: MIT Press.
4. Bishop, C. M. (1999b). Variational principal components. Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99 (pp. 509-514).
5. DeCoste, D. (2006). Collaborative prediction using ensembles of maximum margin matrix factorization. Proceedings of the International Conference in Machine Learning (pp. 249-256). Omnipress.
6. Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. KDD'08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).
7. Lawrence, N. D. (2005). Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research, 6, 1783-1816.
8. Marlin, B. (2004a). Collaborative filtering: A machine learning perspective. Master's thesis, University of Toronto.
9. Marlin, B. (2004b). Modeling user rating profiles for collaborative filtering. Advances in Neural Information Processing Systems (pp. 627-634). Cambridge, MA: MIT Press.
10. Minka, T. P. (2001). Automatic choice of dimensionality for PCA. Advances in Neural Information Processing Systems (pp. 598-604). Cambridge, MA: MIT Press.
11. Park, S.-T., & Pennock, D. M. (2007). Applying collaborative filtering techniques to movie search for better ranking and browsing. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.
12. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Cambridge, MA: MIT Press.
13. Rennie, J. D. M., & Srebro, N. (2005). Fast maximum margin factorization for collaborative prediction. Proceedings of the International Conference in Machine Learning (pp. 713-719).
14. Salakhutdinov, R., & Mnih, A. (2008a). Bayesian probabilistic matrix factorization using MCMC. Proceedings of the International Conference in Machine Learning (pp. 872-879). Omnipress.
15. Salakhutdinov, R., & Mnih, A. (2008b). Probabilistic matrix factorization. Advances in Neural Information Processing Systems (pp. 1257-1264). Cambridge, MA: MIT Press.
16. Schölkopf, B., & Smola, A. J. (2001). Learning with kernels. Cambridge, MA: MIT Press.
17. Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society, B, 6, 611-622.