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Collaborative Filtering for a Distributed Smart IC Card System

  • Eiji Murakami
  • Takao Terano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2132)

Abstract

Collaborative filtering, often used in E-commerce applications, is a method to cluster similar users based on their profiles, characteristics or attitudes on specific subjects. This paper proposes a novel method to implement dynamic collaborative filtering by Genetics-based machine learning, in which we employ Learning Classifier Systems extended to multiple environments. The proposed method is used in a yet another mobile agent system: a distributed smart IC card system. The characteristics of the proposed method are summarized as follows: (1) It is effective in distributed computer environments with PCs even for small number of users. (2) It learns users’ profiles from the individual behaviors of them then generates the recommendation and advices for each user. (3) The results are automatically accumulated in a local system on a PC, then they are distributed via smart IC cards while the users are interacting with the system. The method has been implemented and validated in Group Trip Advisor prototype: a PC-based distributed recommendor system for travel information.

Keywords

Collaborative Filter Learn Classifier System Rule Exchange Digital City Mobile Agent System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Shardanand, U. andP. Maes, Social Information Filtering: Algorithms for Automating ‘Word of Mouth’. Proceedings of the CHI-95 (ACM Press), 1995Google Scholar
  2. [2]
    Paul Resnick and Hal R. Varian, Recommender Systems. Commnications of the ACM, Vol. 40, No. 3, p56–58, 1997CrossRefGoogle Scholar
  3. [3]
    Paul Resnick, Neophytos Iacovou, Mitesh Suchak, et al., GroupLends: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of the Conference on Conputer Supported Cooperative Work, 1994, p 175–186Google Scholar
  4. [4]
    NetPerceptions Inc., Recommendation Engine White Paper. http://www.netperceptions.com/literature/content/recommendation.pdf, 2000
  5. [5]
    K. Takadama, T. Terano, K. Shimohara, K. Hori, S. Nakasuka: Making Organizational Learning Operational: Implication from Learning Classifier System. J. Computational and Mathematical Organization Theory, Vol. 5, No. 3, pp. 229–252, 1999.zbMATHCrossRefGoogle Scholar
  6. [6]
    T. Terano, T. Nishimura, E. Murakami, Y. Ishino: Fairy in a Smart IC Card: Interfacing People, Town, and Digital City. in T. Ishida and K. Isbister Eds, Digital Cities: Experiences, Technologies and Future Perspectives, Lecture Notes in Computer Science 1765, Springer-Verlag, pp. 378–390, 2000.Google Scholar
  7. [7]
    Kim, D.: The Link between Individual and Organizational Learning. Sloan Management Review, Fall, pp. 37–50, 1993.Google Scholar
  8. [8]
    H.A. Simon: Sciences □ of □ Artificial, MIT Press, 1984Google Scholar
  9. [9]
    U.M. Fayyad et. Al.: Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996Google Scholar
  10. [10]
    Ishida, T. (ed.): Community Computing and Support Systems-Social Interaction in Networked Communities. Springer-Verlag Lecture Notes in Computer Science, Vol. 1519 (1998)Google Scholar
  11. [11]
    K.A. De Jong: An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Doctoral dissertation, University of Michigan, 1975Google Scholar
  12. [12]
    R. L. Riolo: Bucket brigade performance: Long sequence-of-classifiers, genetic algorithms and their applications. Proceedings of the Second International Conference on Genetic Algorithms, pp. 184–195, 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Eiji Murakami
    • 1
  • Takao Terano
    • 2
  1. 1.Yamatake CorporationTokyoJapan
  2. 2.University of TsukubaTokyoJapan

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