Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 2052-2057

  Li, Bin ^a   Yang, Qiang ^b   Xue, Xiangyang ^a  

^a FUDAN UNIVERSITY   (China)

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

Author keywords

[No Author keywords available]

Indexed keywords

WEBSITES;

DATA SPARSITY PROBLEMS; EMPIRICAL TEST; MOVIE RATINGS; RATING PATTERN; SPARSITY PROBLEMS; STATE OF THE ART; TARGET DOMAIN; TARGET MATRICES;

COLLABORATIVE FILTERING;

EID: 77951196813     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Gang Chen, Fei Wang, and Changshui Zhang. Collaborative filtering using orthogonal nonnegative matrix tri-factorization. In Proc. of the IEEE Int'l Conf. on Data Mining Workshops, pages 303-308, 2007.
2. Maurice Coyle and Barry Smyth. Web search shared: Social aspects of a collaborative, community-based search network. In Proc. of the 15th Int'l Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems, pages 103-112, 2008.
3. Chris Ding, Tao Li, Wei Peng, and Haesun Park. Orthogonal nonnegative matrix tri-factorizations for clustering. In Proc. of the 12th ACM SIGKDD Int'l Conf., pages 126-135, 2006.
4. Thomas George and Srujana Merugu. A scalable collaborative filtering framework based on co-clustering. In Proc. of the Fifth IEEE Int'l Conf. on Data Mining, pages 625-628, 2005.
5. Thomas Hofmann and Jan Puzicha. Latent class models for collaborative filtering. In Proc. of the 16th Int'l Joint Conf. on Artificial Intelligence, pages 688-693, 1999.
6. Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Ng. Self-taught learning: Transfer learning from unlabeled data. In Proc. of the 24th Int'l Conf. on Machine Learning, pages 759-766, 2007.
7. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proc. of the ACM Conf. on Computer Supported Cooperative Work, pages 175-186, 1994.
8. Michael T. Rosenstein, Zvika Marx, and Leslie Pack Kaelbling. To transfer or not to transfer. In Proc. of the NIPS Workshop on Inductive Transfer: 10 Years Later, 2005.
9. Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. of the 10th Int'l World Wide Web Conf., pages 285-295, 2005.
10. Luo Si and Rong Jin. Flexible mixture model for collaborative filtering. In Proc. of the 20th Int'l Conf. on Machine Learning, pages 704-711, 2003.
11. Nathan Srebro and Tommi Jaakkola. Weighted low-rank approximations. In Proc. of the 20th Int'l Conf. on Machine Learning, pages 720-727, 2003.
12. David C. Wilson, Barry Smyth, and Derry O'sullivan. Sparsity reduction in collaborative recommendation: A case-based approach. Int'l Journal of Pattern Recognition and Artificial Intelligence, 17(5):863-884, 2003.
13. Gui-Rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-Jun Zeng, Yong Yu, and Zheng Chen. Scalable collaborative filtering using cluster-based smoothing. In Proc. of the 28th SIGIR Conf., pages 114-121, 2005.