Improving Recommendation Ranking by Learning Personal Feature Weights-Reference-Cited by-同舟云学术

Improving Recommendation Ranking by Learning Personal Feature Weights

Published:2004 Issue: Volume: Page:560-572
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Coyle Lorcan,Cunningham Pádraig

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-28631-8_41.pdf

Reference15 articles.

1. Bergmann, R., Richter, M.M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-Oriented Matching: A New Research Direction for Case-Based Reasoning. In: Schnurr, H.-P., Staab, S., Studer, R., Stumme, G., Sure, Y. (eds.) Proceedings of the 9th German Workshop on Case-Based Reasoning, GWCBR 2001, Baden-Baden, Germany, Professionelles Wissensmanagement, pp. 264–274. Shaker Verlag (2001)

2. Lecture Notes in Computer Science;A. Bonzano,1997

3. Bradley, K., Smyth, B.: Improving Recommendation Diversity. In: O’Connor, D. (ed.) Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, vol. 2001, pp. 85–94 (2001)

4. Branting, L.K.: Learning Feature Weights from Customer Return-Set Selections. The Journal of Knowledge and Information Systems (KAIS) 6(2) (2003) (March 2004)

5. Burke, R., Hammond, K., Young, B.: The FindMe Approach to Assisted Browsing. IEEE Expert 12(4), 32–40 (1997)

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