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

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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.

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Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, P. R. China

    Cane Wing-ki Leung, Stephen Chi-fai Chan & Fu-lai Chung

Authors
  1. Cane Wing-ki Leung
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  2. Stephen Chi-fai Chan
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  3. Fu-lai Chung
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Correspondence to Cane Wing-ki Leung.

Additional information

Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work.

Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing.

Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.

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Leung, C.Wk., Chan, S.Cf. & Chung, Fl. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10, 357–381 (2006). https://doi.org/10.1007/s10115-006-0002-1

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