Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2004, Pages 156-157

  Si, Luo ^a   Jin, Rong ^b  

^a Carnegie Mellon University   (United States)

^b Michigan State University   (United States)

Author keywords

Unified filtering

Indexed keywords

ALGORITHMS; COMPUTER SCIENCE; INFORMATION ANALYSIS; MATHEMATICAL MODELS; PROBABILITY; PROBLEM SOLVING;

COLLABORATIVE ENSEMBLE LEARNING (CEL); CONTENT INFORMATION; CONTENT-BASED FILTERING (CBF); UNIFIED FILTERING;

CONTENT BASED RETRIEVAL;

EID: 18744367839     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1031171.1031201     Document Type: Conference Paper

References (4)

1. J. S. Breese, D. Heckerman & C. Kadie. (1998). Empirical Analysis of Predictive Algorthms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.
2. P. Melville, R. Mooney & R. Nagarajan. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of Conference on Artificial Intelligence.
3. L. Si & R. Jin. (2003). Flexible Mixture Model for Collaborative Filtering. In Proceedings of the 20th International Conference on Machine Learning.
4. K. Yu, A. Schwaighofer, V. Tresp, W. Y. Ma & H. J. Zhang (2003). Collaborative ensemble learning: combining collaborative and content-based information filtering via Hierarchical Bayes. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence.