Lightweight collaborative filtering method for binary-encoded data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2168, Issue , 2001, Pages 484-491

  Weiss, Sholom M ^a   Indurkhya, Nitin ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; PURCHASING; SALES; WEBSITES;

COLLABORATIVE FILTERING METHODS;

COLLABORATIVE FILTERING;

EID: 79957494767     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44794-6_40     Document Type: Conference Paper

References (11)

1. D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. CACM, 35(12):61–70, 1992.
2. T. Cover and P. Hart. Nearest neighbour pattern classification. IEEE Transactions on Information Theory, 13:21–27, 1967.
3. J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in AI, 1998.
4. D. Chickering, D. Heckerman, and C. Meek. A bayesian approach to learning bayesian networks with local structure. In Proceedings of the Thirteenth Conference on Uncertainty in AI, 1997.
5. 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 Conference on Computer Supported Cooperative Work, pages 175–186, 1994.
6. G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983.
7. S. Weiss, H. White, and C. Apt´e. Lightweight document clustering. In Proceedings of PKDD-2000, pages 665–672. Springer, 2000.
8. S. Weiss, B. White, C. Apte, and F. Damerau. Lightweight document matching for help-desk applications. IEEE Intelligent Systems, pages 57–61, 2000.
9. D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1:49–75, 2000.
10. G. Karypis. Evaluation of item-based top-n recommendation algorithms. Technical Report 046, Department of Computer Science, University of Minnesota, 2000.
11. M. Deshpande and G. Karypis. Selective markov models for predicting web-page accesses. In Proceedings of SIAM Data Mining Conference, 2001.