A Multi-Agent Recommender System

  • A. Jorge Morais
  • Eugénio Oliveira
  • Alípio Mário Jorge
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

The large amount of pages in Websites is a problem for users who waste time looking for the information they really want. Knowledge about users’ previous visits may provide patterns that allow the customization of the Website. This concept is known as Adaptive Website: a Website that adapts itself for the purpose of improving the user’s experience. Some Web Mining algorithms have been proposed for adapting a Website. In this paper, a recommender system using agents with two different algorithms (associative rules and collaborative filtering) is described. Both algorithms are incremental and work with binary data. Results show that this multi-agent approach combining different algorithms is capable of improving user’s satisfaction.

Keywords

Association Rule Recommender System Collaborative Filter Autonomic Computing Individual Algorithm 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • A. Jorge Morais
    • 1
    • 4
    • 5
  • Eugénio Oliveira
    • 1
    • 3
  • Alípio Mário Jorge
    • 2
    • 4
  1. 1.Faculty of EngineeringThe University of PortoPortoPortugal
  2. 2.Faculty of ScienceThe University of PortoPortoPortugal
  3. 3.Laboratory of Artificial Intelligence and Computer Science (LIACC)PortoPortugal
  4. 4.Laboratory of Artificial Intelligence and Decision Support (LIAAD – INESC Porto L. A.)PortoPortugal
  5. 5.Universidade Aberta (Portuguese Open University)LisbonPortugal

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