A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

Cane Wing Ki Leung, Stephen Chi Fai Chan, Fu Lai Korris Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

99 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)357-381
Number of pages25
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 1 Oct 2006


  • Collaborative filtering
  • Fuzzy association rule mining
  • Recommender systems
  • Similarity

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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