One-class collaborative filtering with random graphs

WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Volumn , Issue , 2013, Pages 999-1008

  Paquet, Ulrich ^a   Koenigstein, Noam ^b  

^a MICROSOFT   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

One class collaborative filtering; Random graph; Variational inference

Indexed keywords

DISTRIBUTED COMPUTER SYSTEMS; GRADIENT METHODS; GRAPH THEORY; STOCHASTIC SYSTEMS; WORLD WIDE WEB;

BAYESIAN GENERATIVE MODELS; CORE COMPONENTS; DISTRIBUTED LEARNING; FINE GRAINED; RANDOM GRAPHS; STATE OF THE ART; STOCHASTIC GRADIENT DESCENT; VARIATIONAL INFERENCE;

COLLABORATIVE FILTERING;

EID: 84887582619     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2488388.2488475     Document Type: Conference Paper

References (26)

1. S. Amari. Natural gradient works efficiently in learning. Neural Computation, 10(2):251-276, 1998.
2. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
3. G. Dror, N. Koenigstein, and Y. Koren. Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy. In Proc. 5th ACM Conference on Recommender Systems, 2011.
4. G. Dror, N. Koenigstein, Y. Koren, and M. Weimer. The Yahoo! music dataset and KDD-Cup'11. Journal Of Machine Learning Research, 18:3-18, 2012.
5. Z. Gantner, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. Personalized ranking for non-uniformly sampled items. Journal of Machine Learning Research, 18:231-247, 2011.
6. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: A free recommender system library. In 5th ACM International Conference on Recommender Systems, 2011.
7. Y. F. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining, 2008.
8. T. Jaakkola and M. Jordan. A variational approach to Bayesian logistic regression problems and their extensions. In Artificial Intelligence and Statistics, 1996.
9. M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. An introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999.
10. N. Koenigstein, N. Nice, U. Paquet, and N. Schleyen. The Xbox recommender system. In Proc. 6th ACM Conference on Recommender Systems, 2012.
11. Y. Koren. The BellKor solution to the Netflix Grand Prize. 2009.
12. H. J. Kushner and G. G. Yin. Stochastic Approximation and Recursive Algorithms and Applications. Springer, 2003.
13. D. J. C. MacKay. The evidence framework applied to classification networks. Neural Computation, 4(5):698-714, 1992.
14. R. M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 1993.
15. M. E. J. Newman, S. H. Strogatz, and D. J. Watts. Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E, 64:026118, 2001.
16. M. E. J. Newman, D. J. Watts, and S. H. Strogatz. Random graph models of social networks. Proc. Natl. Acad. Sci. USA, 99:2566-â Ǎ Ş2572, 2002.
17. K. Palla, D. A. Knowles, and Z. Ghahramani. An infinite latent attribute model for network data. In 29th International Conference on Machine Learning, 2012.
18. R. Pan and M. Scholz. Mind the gaps: Weighting the unknown in large-scale one-class collaborative filtering. In KDD, pages 667-675, 2009.
19. R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In IEEE International Conference on Data Mining, pages 502-511, 2008.
20. U. Paquet, B. Thomson, and O. Winther. A hierarchical model for ordinal matrix factorization. Statistics and Computing, 22(4):945-957, 2012.
21. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Uncertainty in Artificial Intelligence, pages 452-461, 2009.
22. H. Robbins and S. Monro. A stochastic approximation method. The Annals of Mathematical Statistics, 22(3):400-407, 1951.
23. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th International Conference on Machine Learning, pages 880-887, 2008.
24. M. Sato. Online model selection based on the variational Bayes. Neural Computation, 13(7):1649-1681, 2001.
25. D. Stern, R. Herbrich, and T. Graepel. Matchbox: Large scale Bayesian recommendations. In International World Wide Web Conference, 2009.
26. M. P. H. Stumpf and C. Wiuf. Sampling properties of random graphs: The degree distribution. Phys. Rev. E, 72:036118, 2005.