Belief Revision and Text Mining for Adaptive Recommender Agents

  • Raymond Y. K. Lau
  • Peter van den Brand
Conference paper

DOI: 10.1007/978-3-540-39592-8_32

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2871)
Cite this paper as:
Lau R.Y.K., van den Brand P. (2003) Belief Revision and Text Mining for Adaptive Recommender Agents. In: Zhong N., Raś Z.W., Tsumoto S., Suzuki E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science, vol 2871. Springer, Berlin, Heidelberg

Abstract

With the rapid growth of the number of electronic transactions conducted over the Internet, recommender systems have been proposed to provide consumers with personalized product recommendations. This paper illustrates how belief revision and text mining can be used to improve recommender agents’ prediction effectiveness, learning autonomy, adaptiveness, and explanatory capabilities. To our best knowledge, this is the first study of integrating text mining techniques and belief revision logic into a single framework for the development of adaptive recommender agents.

Keywords

Belief Revision Text Mining Recommender Agents 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Raymond Y. K. Lau
    • 1
  • Peter van den Brand
    • 1
  1. 1.Centre for Information Technology Innovation, Faculty of Information TechnologyQueensland University of TechnologyBrisbaneAustralia

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