An automatic weighting scheme for collaborative filtering

Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2004, Pages 337-344

  Jin, Rong ^a   Chai, Joyce Y ^a   Si, Luo ^b  

^a Michigan State University   (United States)

^b Carnegie Mellon University   (United States)

Author keywords

Collaborative filtering; Item weighting scheme; Leave one out method; Memory based approach

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATABASE SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; PROBLEM SOLVING;

COLLABORATIVE FILTERING (CF); ITEM WEIGHTING SCHEME; MEMORY-BASED APPROACH; PEARSON CORRELATION COEFFICIENT (PCC);

INFORMATION SCIENCE;

EID: 8644230345     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1008992.1009051     Document Type: Conference Paper

References (20)

1. J- S. Breese, D. Heckerman and C. Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI), 1998.
2. nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 1999.
3. rd Annual International Conference on Researech and Development in Information Retrieval (SIGIR), 2000.
4. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work, pages 175-186, 1994.
5. T. Hofmann and J. Puzicha, Latent Class Models for Collaborative Filtering, In Proceedings of International Joint Conference on Artificial Intelligence (UAI), 1999.
6. I. M. Soboroff and Charles K.Nicholas. Combining Content and Collaboration in Text Filtering. In Proceedings of the IJCAI'99 Workshop on Machine Learning for Information Filtering, 1999
7. P. Melville, R. J. Mooney, R. Nagarajan, Content-Boosted Collaborative Filtering for Improved Recommendations. In Proceedings of the the Eighteenth National Conference on Artificial Intelligence (AAAI), 2002.
8. rd Annual International Conference on Researech and Development in Information Retrieval (SIGIR), 2000.
9. http://www.cs.usyd.edu.au/~irena/movie_data.zip
10. http://research.compaq.com/SRC/eachmovie
11. A. Dempster, N. Laird and D. Rubin. Maximum Likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B 39:1-38, 1977.
12. D. M. Pennock, E. Horvitz, S. Lawrence and C. L. Giles, Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach, in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI), 2000.
13. nd International Workshop on Management of Information on the Web: Web Data and Text Mining (MIW'01), 2001
14. T. Hofmann, Gaussian Latent Semantic Models for Collaborative Filtering, In the Proceedings of the 26th Annual International ACM SIGIR Conference, 2003
15. L. Si and R. Jin, A Flexible Mixture Model for Collaborative Filtering, In the Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 2003.
16. A. Berger, V. Pietra and S. Pietra, A Maximum Entropy Approach to Natural Language Processing. In Computational Linguistics, 22:39-71, 1996.
17. V. Rejisbergen, Information Retrieval, 1979
18. A.Y. Ng and M.I. Jordan. On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes. NIPS 14, 2002
19. G. Salton and C. Buckley, Term-weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24, 513-523, 1998
20. D.D. Lewis, Y. Yang, T. Rose and F. Li. RCV1 : A New Text Categorization Test Collection. Journal of Machine Learning Research 2003.