A cost-sensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce-Reference-Cited by-同舟云学术

A cost-sensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce

Published:2012-04 Issue:1 Volume:53 Page:245-256
ISSN:0167-9236
Container-title:Decision Support Systems
language:en
Short-container-title:Decision Support Systems

Author:

Lee Yen-Hsien,Hu Paul Jen-Hwa,Cheng Tsang-Hsiang,Hsieh Ya-Fang

Funder

National Science Council of the Republic of China

Publisher

Elsevier BV

Subject

Information Systems and Management,Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Information Systems,Management Information Systems

Reference82 articles.

1. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions;Adomavicius;IEEE Transactions on Knowledge and Data Engineering,2005

2. Athena: mining-based interactive management of text databases;Agrawal,2000

3. Comparing feature-based and clique-based user models for movie selection;Alspector,1998

4. Collaborative filtering via concept decomposition on the netflix dataset;Ampazis,2008

5. Fab: content-based, collaborative recommendation;Balabanovic;Communications of the ACM,1997

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