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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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References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large databases, Santiago de Chile, Chile, pp 487–499

  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD international conference on management of data, Washington, DC, pp 207–216

  3. Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the 15th national conference on artificial intelligence, Madison, WI, pp 714–720

  4. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceeding of the 14th conference on uncertainty in artificial intelligence, Madison, Wisconsin, pp 43–52

  5. Burke R (2000) Knowledge-based recommender systems. In: Kent A (ed) Encyclopedia of library and information systems, vol 69, Suppl 32. Marcel Dekker, New York

  6. Deshpande M, Karypis G (2004) Item-based Top-N recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

    Article  Google Scholar 

  7. Fu AWC, Wong MH, Sze SC, et al (1998) Finding fuzzy sets for the mining of fuzzy association rules for numerical attributes. In: Proceedings of the 1st international symposium on intelligent data engineering and learning, Hong Kong SAR, P. R. China, pp 263–268

  8. Fu X, Budzik J, Hammond KJ (2000) Mining navigation history for recommendation. In: Proceedings of the 2000 international conference on intelligent user interfaces, New Orleans, LA, USA, pp 106–112

  9. Goldberg D, Nichols D, Oki B, et al (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  10. Goldberg K, Roeder T, Gupta D, et al (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retriev 4(2):133–151

    Article  MATH  Google Scholar 

  11. GroupLens: MovieLens Dataset: http://www.grouplens.org

  12. Gyenesei A (2000) A fuzzy approach for mining quantitative association rules. TUCS Technical Report No 336, Turku Centre for Computer Science

  13. Gyenesei A, Teuhola J (2001) Interestingness measures for fuzzy association rules. In: Proceedings of the 5th European conference on principles of data mining and knowledge discovery, Freiburg, Germany, pp 152–164

  14. Han J, Kamber M (2000) Data mining: Concepts and techniques. Morgan Kaurmann

  15. Herlocker J, Konstan J, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retriev 5:287–310

    Article  Google Scholar 

  16. Hong T-P, Lee C-Y (1998) Learning fuzzy knowledge from training examples. In: Proceedings of the 1998 ACM CIKM international conference on information and knowledge management, Bethesda, Maryland, pp 161–166

  17. Huang Z, Chung W, Chen H (2004) A graph model for e-commerce recommender systems. J Am Soc Inf Sci Technol 55(3):259–274

    Article  Google Scholar 

  18. Kim BM, Li Q, Kim JW, et al (2004) A New collaborative recommender system addressing three problems. In: Proceeding of the 8th Pacific Rim international conference on artificial intelligence, Auckland, New Zealand, pp 495–504

  19. Kim C, Kim J (2003) A recommendation algorithm using multi-level association rules. In: Proceeding of the IEEE/WIC international conference on web intelligence, Halifax, Canada, pp 524–527

  20. Konstan J, Miller B, Maltz, D, et al (1997) Applying collaborative filtering to usenet news. Commun ACM 40(3):77–87

    Article  Google Scholar 

  21. Kuok CM, Fu A, Wong MH (1998) Mining fuzzy association rules in databases. ACMMOD Rec 27(1):41–46

    Google Scholar 

  22. Lee JH, Hyung LK (1997) An extension of association rules using fuzzy sets. In: Proceedings of the 7th international fuzzy systems association world congress, Prague, Czech Republic, pp 399–402

  23. Leung CWK, Chan SCF, Chung KFL (2004) Towards collaborative travel recommender systems. In: Proceedings of the 4th international conference on electronic business, Beijing, P. R. China, pp 445–451

  24. Lin W, Alvarez SA, Ruiz C (2002) Efficient adaptive-support association rule mining for recommender systems. Data Min Knowl Dis 6(1):83–105

    Article  MathSciNet  Google Scholar 

  25. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. Industry report, IEEE, pp 76–80

  26. Maltz D, Ehrlich K (1995) Pointing the way: active collaborative filtering. In: Proceedings of ACM CHI'95 conference on human factors in computing systems, Denver, CO, pp 202–209

  27. McJones P (1997) Each Movie Dataset: http://research.compaq.com/SRC/eachmovie/

  28. Miyahara K, Pazsani MJ (2000). Collaborative filtering with the simple Bayesian classifier. In: Proceedings of the 6th Pacific Rim international conference on artificial intelligence, Melbourne, Australia, pp 679–689

  29. Perny P, Zucker J-D (1999) Collaborative filtering methods based on fuzzy preference relations. In: Proceedings of the 4th meeting of the Euro working group on fuzzy sets, Budapest, Hungary, pp 279–285

  30. Resnick P, Iacovou N, Suchak M, et al (1994) GroupLens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM conference on computer supported cooperative work, Chapel Hill, NC, pp 175–186

  31. Sarwar BM, Konstan J, Borchers A, et al (1998) Using filtering agents to improve prediction quality in the groupLens research collaborative filtering system. In: Proceedings of the ACM conference on computer supported cooperative work, Seattle, WA, USA, pp 345–354

  32. Shardanand U, Maes P (1995) Social information filtering: algorithms for automating ‘Word of Mouth’. In: Proceedings of ACM CHI'95 conference on human factors in computing systems, Denver, CO, pp 210–217

  33. Silvestri F, Baraglia R, Palmerini P (2004) Online generation of suggestions for web users. J Digital Inf Manage 2(2):104–108

    Google Scholar 

  34. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relationship tables. In: Proceedings of the ACM SIGMOD international conference on management of data, Montreal, Canada, pp 1–12

  35. Zaki MJ (2000) Scalable Algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372–390

    Article  MathSciNet  Google Scholar 

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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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