TALMUD: Transfer learning for multiple domains

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 425-434

  Moreno, Orly ^a   Shapira, Bracha ^a   Rokach, Lior ^a   Shani, Guy ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

collaborative filtering; cross domains; recommender systems; transfer learning

Indexed keywords

COLLABORATIVE FILTERING; LEARNING SYSTEMS;

COLLABORATIVE RECOMMENDER SYSTEMS; CROSS-DOMAIN; LEARNING TECHNIQUES; MACHINE LEARNING TECHNIQUES; MULTIPLE DOMAINS; SINGLE DOMAINS; SPARSITY PROBLEMS; TARGET DOMAIN; TRANSFER LEARNING; USAGE DATA;

RECOMMENDER SYSTEMS;

EID: 84871075003     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2396817     Document Type: Conference Paper

References (20)

1. S. Berkovsky, T. Kuflik, and F. Ricci. Cross-domain mediation in collaborative filtering. User Modeling 2007, pages 355-359, 2007.
2. J. Breese, D. Heckerman, C. Kadie, et al. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on Uncertainty in Artificial Intelligence, pages 43-52, 1998.
3. B. Cao, N. Liu, and Q. Yang. Transfer learning for collective link prediction in multiple heterogeneous domains. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel. Citeseer, 2010.
4. W. Dai, G. Xue, Q. Yang, and Y. Yu. Co-clustering based classification for out-of-domain documents. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 210-219. ACM, 2007.
5. C. Desrosiers and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. Recommender Systems Handbook, pages 107-144, 2011.
6. T. Dietterich. Overfitting and undercomputing in machine learning. ACM Computing Surveys, 27(3):326-327, 1995.
7. C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix t-factorizations for clustering. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 126-135. ACM, 2006.
8. L. Getoor and C. Diehl. Link mining: a survey. ACM SIGKDD Explorations Newsletter, 7(2):3-12, 2005.
9. T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In International Joint Conference on Artificial Intelligence, volume 16, pages 688-693. LAWRENCE ERLBAUM ASSOCIATES LTD, 1999.
10. H. Kiers. Setting up alternating least squares and iterative majorization algorithms for solving various matrix optimization problems. Computational statistics & data analysis, 41(1):157-170, 2002.
11. R. Kohavi and D. Sommerfield. Feature subset selection using the wrapper method: Overfitting and dynamic search space topology. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pages 192 U-197, 1995.
12. Y. Koren and R. Bell. Advances in collaborative filtering. Recommender Systems Handbook, pages 145-186, 2011.
13. B. Li. Cross-domain collaborative filtering: A brief survey. In 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, pages 1085-1086. IEEE, 2011.
14. B. Li, Q. Yang, and X. Xue. Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In Proceedings of the 21st international jont conference on Artificial intelligence, pages 2052-2057. Morgan Kaufmann Publishers Inc., 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 Annual International Conference on Machine Learning, pages 617-624. ACM, 2009.
16. S. Pan and Q. Yang. A survey on transfer learning. Knowledge and Data Engineering, IEEE Transactions on, 22(10):1345-1359, 2010.
17. W. Pan, E. Xiang, N. Liu, and Q. Yang. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the 24rd AAAI Conference on Artificial Intelligence, 2010.
18. G. Shani and A. Gunawardana. Evaluating recommendation systems. Recommender Systems Handbook, pages 257-297, 2011.
19. G. Xue, W. Dai, Q. Yang, and Y. Yu. Topic-bridged plsa for cross-domain text classification. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 627-634. ACM, 2008.
20. Y. Zhang, B. Cao, and D. Yeung. Multi-domain collaborative filtering. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, California, USA, 2010.