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Multi-Agent Web Recommendations

  • Joaquim Neto
  • A. Jorge Morais
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 290)

Abstract

Due to the large amount of pages in Websites it is important to collect knowledge about users’ previous visits in order to provide patterns that allow the customization of the Website. In previous work we proposed a multi-agent approach using agents with two different algorithms (associative rules and collaborative filtering) and showed the results of the offline tests. Both algorithms are incremental and work with binary data. In this paper we present the results of experiments held online. Results show that this multi-agent approach combining different algorithms is capable of improving user’s satisfaction.

Keywords

Association Rule Recommender System MultiAgent System Collaborative Filter Autonomic Computing 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Joaquim Neto
    • 3
    • 4
  • A. Jorge Morais
    • 1
    • 2
    • 4
  1. 1.Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Laboratory of Artificial Intelligence and Decision Support (LIAAD – INESC TEC L. A.)PortoPortugal
  3. 3.National Laboratory of Civil Engineering (LNEC)LisbonPortugal
  4. 4.Universidade Aberta (Portuguese Open University)LisbonPortugal

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