Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29371
Title: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
Authors: Leung, CWK
Chan, SCF 
Chung, FL 
Keywords: Collaborative filtering
Fuzzy association rule mining
Recommender systems
Similarity
Issue Date: 2006
Publisher: Springer
Source: Knowledge and information systems, 2006, v. 10, no. 3, p. 357-381 How to cite?
Journal: Knowledge and information systems 
Abstract: The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.
URI: http://hdl.handle.net/10397/29371
ISSN: 0219-1377
EISSN: 0219-3116
DOI: 10.1007/s10115-006-0002-1
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