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Item-based collaborative filtering recommendation algorithms
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Source International World Wide Web Conference archive
Proceedings of the 10th international conference on World Wide Web table of contents
Hong Kong, Hong Kong
Pages: 285 - 295  
Year of Publication: 2001
ISBN:1-58113-348-0
Authors
Badrul Sarwar  GroupLens Research Group/Army HPC Research Center, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
George Karypis  GroupLens Research Group/Army HPC Research Center, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
Joseph Konstan  GroupLens Research Group/Army HPC Research Center, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
John Reidl  GroupLens Research Group/Army HPC Research Center, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGLINK: Hypertext, Hypermedia, and Web
IW3C2 : International World Wide Web Conference Committee
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 83,   Downloads (12 Months): 594,   Citation Count: 144
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference onUncertainty in Artificial Intelligence, pp. 43-52.
 
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Cureton, E. E., and D'Agostino, R. B. (1983). Factor Analysis: An Applied Approach. Lawrence Erlbaum associates pubs. Hillsdale, NJ.
 
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Karypis, G. (2000). Evaluation of Item-Based Top-N Recommendation Algorithms. Technical Report CS-TR-00-46, Computer Science Dept., University of Minnesota.
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Ling, C. X., and Li, C. (1998). Data Mining for Direct Marketing: Problems and Solutions. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 73-79.
 
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Peppers, D., and Rogers, M. (1997). The One to One Future : Building Relationships One Customer at a Time. Bantam Doubleday Dell Publishing.
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Reichheld, F. R., and Sasser Jr., W. (1990). Zero Defections: Quality Comes to Services. Harvard Business School Review, 1990(5): pp. 105-111.
 
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Reichheld, F. R. (1993). Loyalty-Based Management. Harvard Business School Review, 1993(2): pp. 64-73.
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Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. (2000). Application of Dimensionality Reduction in Recommender System{A Case Study. InACM WebKDD 2000 Workshop.
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Ungar, L. H., and Foster, D. P. (1998) Clustering Methods for Collaborative Filtering. In Workshop on Recommender Systems at the 15th National Conference onArtificial Intelligence.

CITED BY  144