Group typical preference extraction using collaborative filtering profile

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

Volumn 2738, Issue , 2003, Pages 414-423

  Ko, Su Jeong ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENTROPY; MINING; VECTOR SPACES;

COLLABORATIVE FILTERING SYSTEMS; COLLABORATIVE USERS; DEGREE OF CORRELATIONS; DYNAMIC RECOMMENDATIONS; MINING METHODS; PREFERENCE EXTRACTIONS; VECTOR SPACE MODELS;

COLLABORATIVE FILTERING;

EID: 35048889288     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (17)

1. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994.
2. K. Alsabti, S. Ranka, and V. Singh,.An Efficient K-Means Clustering Algorithm,. http://www.cise.ufl.edu/ranka/, 1997.
3. C. Basu, H. Hirsh, and W. W. Cohen,.Recommendation as classification:Using social and content-based information in recommendation,. In proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 714-720, Madison, WI, 1998.
4. John. S. Breese and C. Kadie,.Empirical Analysis of Predictive Algorithms for Collaborative Filtering,. Proceedings of the Conference on Uncertainty in Artificial Intelligence, Madison, WI, 1998.
5. D. Billsus and M. J. Pazzani,.Learning collaborative information filters,. In proceedings of the International Conference on Machine Learning, 1998.
6. J. Delgado and N. Ishii,.Formal Models for Learning of User Preferences, a Preliminary Report,. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, July, 1999.
7. Inha University,.Intelligent Information Retrieval System centering User., Technical research report, 1997.
8. A. Kohrs and B. Merialdo,.USING CATEGORY-BASED COLLABORATIVE FILTERING IN THE ACTIVE WEBMUSEUM,. Proceedings of the IEEE International Conference on Multimedia and Expo - Vol. 1, 2000.
9. Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J.,.Application of Dimensionality Reduction in Recommender System-A Case Study,. In ACM WebKDD 200 Web Mining for E-Commerce Workshop, 2000.
10. Taek-Hun Kim, Young-Suk Ryu, Seok-In Park, Sung-Bong Yang,.An Improved Recommendation Algorithm in Collaborative Filtering,. EC-Web 2002, pp. 254-261, 2002.
11. S. J. Ko and J. H. Lee,.Feature Selection using Association Word Mining for Classification,. In Proceedings of the Conference on DEXA2001, LNCS2113, pp. 211-220, 2001.
12. Y. S. Lee and S. W. Lee,.Group Feature Selection using Entropy Weight and SVD,. Transaction of KISS(B), Vol. 29, No. 4, 2002.
13. W. S. Lee,.Collaborative learning for recommender systems,. In Proceedings of the Conference on Machine Learning, 1997.
14. G. J. McLachlan and T. Krishnan, The EM Algorithm and Extensions, New
15. M. Pazzani, D. Billsus, Learning and Revising User Profiles: The Identification of Interesting Web Sites, Machine Learning, Kluwer Academic Publishers, pp. 313-331, 1997.
16. Badrul Sarwar, George Karypis, Josephp Konstan, and John Ridedl,.Analysis of Recommendation Algorithms for E-Commerce,. Proc. Of The ACM Ecommerce 2000, 2000.
17. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983.