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Immune Learning in a Dynamic Information Environment

  • Nikolaos Nanas
  • Manolis Vavalis
  • Lefteris Kellis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5666)

Abstract

In Adaptive Information Filtering, the user profile has to be able to define and maintain an accurate representation of the user’s interests over time. According to Autopoietic Theory, the immune system faces a similar continuous learning problem. It is an organisationally closed network that reacts autonomously to define and preserve the organism’s identity. Nootropia is a user profiling model, which has been inspired by this view of the immune system. In this paper, we introduce new improvements to the model and propose a methodology for testing the ability of a user profile to continuously learn a user’s changing interests in a dynamic information environment. Comparative experiments show that Nootropia outperforms a popular learning algorithm, especially when more than one topic of interest has to be represented.

Keywords

Relevant Document Relevance Score User Interest Immune Network Immune Repertoire 
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 2009

Authors and Affiliations

  • Nikolaos Nanas
    • 1
  • Manolis Vavalis
    • 2
  • Lefteris Kellis
    • 2
  1. 1.Lab for Information Systems and ServicesCentre for Research and Technology, Thessaly (CE.RE.TE.TH)Greece
  2. 2.Computing and Telecomunications DepartmentUniversity of ThessalyGreece

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