Multi-domain collaborative filtering

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Volumn , Issue , 2010, Pages 725-732

  Zhang, Yu ^a   Cao, Bin ^a   Yeung, Dit Yan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FACTORIZATION;

COLLABORATIVE FILTERING METHODS; DATA SPARSITY PROBLEMS; DIFFERENT DOMAINS; LINK FUNCTIONS; MULTI DOMAINS; PROBABILISTIC FRAMEWORK; PROBABILISTIC MATRIX FACTORIZATIONS; SIMILAR INTERESTS;

COLLABORATIVE FILTERING;

EID: 80053167162     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. C. Basu, H. Hirsh, and W. W. Cohen. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 15th National Conference on Artificial Intelligence, pages 714-720, Madison, Wis, USA, 1998.
2. D. Billsus and M. J. Pazzani. Learning collaborative information filters. In Proceedings of the 15th International Conference on Machine Learning, pages 46-54, Madison, Wisconson, USA, 1998.
3. C. Boutilier, R. Zemel, and B. Marlin. Active collaborative filtering. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, pages 98-106, San Francisco, CA, 2003.
4. J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 43-52, Madison, WI, 1998.
5. D. DeCoste. Collaborative prediction using ensembles of maximum margin matrix factorizations. In Proceedings of the 23rd International Conference on Machine Learning, pages 249-256, Pittsburgh, Pennsylvania, USA, 2006.
6. M. Fazel, H. Hindi, and S. Boyd. A rank minimization heuristic with application to minimum order system approximation. In Proceedings American Control Conference, pages 4734-4739, 2001. (Pubitemid 32837075)
7. M. Fazel, H. Hindi, and S. Boyd. Log-det heuristic for matrix rank minimization with applications to hankel and euclidean distance matrices. In Proceedings American Control Conference, pages 2156-2162, 2003.
8. A. K. Gupta and D. K. Nagar. Matrix variate distributions. Chapman & Hall, 2000.
9. D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for collaborative filtering and data visualization. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pages 264-273, Stanford University, Stanford, California, USA, 2000.
10. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230-237, Berkeley, CA, USA, 1999.
11. R. Jin, L. Si, and C. Zhai. Preference-based graphic models for collaborative filtering. In Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, pages 329-336, Acapulco, Mexico, 2003.
12. Rong Jin and Luo Si. A bayesian approach toward active learning for collaborative filtering. In Proceedings of the 20th Conference in Uncertainty in Artificial Intelligence, pages 278-285, Banff, Canada, 2004.
13. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, 2008.
14. N. D. Lawrence and R. Urtasun. Non-linear matrix factorization with gaussian processes. In Proceedings of the 26th International Conference on Machine Learning, pages 601- 608, Montreal, Quebec, Canada, 2009.
15. B. Li, Q. Yang, and X. Xue. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th International Conference on Machine Learning, pages 617-624, Montreal, Quebec, Canada, 2009.
16. M. R. McLaughlin and J. L. Herlocker. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 329-336, Sheffield, UK, 2004.
17. P. Melville, R. J. Mooney, and R. Nagarajan. Contentboosted collaborative filtering for improved recommendations. In Proceedings of the 8th National Conference on Artificial intelligence, pages 187-192, Edmonton, Alberta, Canada, 2002.
18. D. Pennock, E. Horvitz, S. Lawrence, and C. Giles. Collaborative filtering by personality diagnosis: A hybrid memoryand model-based approach. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pages 473- 480, Stanford University, Stanford, California, USA, 2000.
19. A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, pages 437-444, University of Washington, Seattle, Washington, USA, 2001.
20. J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd International Conference on Machine Learning, pages 713-719, Bonn, Germany, 2005.
21. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1257-1264, Vancouver, British Columbia, Canada, 2007.
22. 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, Helsinki, Finland, 2008.
23. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Itembased collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference, pages 285-295, Hong Kong, 2001.
24. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 650-658, Las Vegas, Nevada, USA, 2008.
25. N. Srebro, J. D. M. Rennie, and T. S. Jaakkola. Maximummargin matrix factorization. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1329-1336, Vancouver, British Columbia, Canada, 2005.
26. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009.
27. M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistic Society, B, 61(3):611-622, 1999.
28. T. Truyen, D. Phung, and S. Venkatesh. Ordinal boltzmann machines for collaborative filtering. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pages 548-556, Corvallis, Oregon, 2009.
29. K. Yu, J. D. Lafferty, S. Zhu, and Y. Gong. Large-scale collaborative prediction using a nonparametric random effects model. In Proceedings of the 26th International Conference on Machine Learning, pages 1185-1192, Montreal, Quebec, Canada, 2009.
30. K. Yu, A. Schwaighofer, V. Tresp, W.-Y. Ma, and H. Zhang. Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, pages 616-623, Acapulco, Mexico, 2003.
31. K. Yu, A. Schwaighofer, V. Tresp, X. Xu, and H.-P. Kriegel. Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engeering, 16(1):56-69, 2004.
32. K. Yu, S. Zhu, J. D. Lafferty, and Y. Gong. Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 211-218, Boston, MA, USA, 2009.