Profile adaptation in adaptive information filtering: An immune inspired approach

Nurulhuda Firdaus Mohd Azmi, Jon Timmis, Fiona Polack

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

8 Citations (SciVal)

Abstract

Within the context of information ltering, learning and adaptation of user pro les is a challenging research area and is, in part, addressed by work in Adaptive Information Filtering (AIF). In order to be effective in a dynamic context, maintaining ltering performance, information ltering systems need to adapt to changes. We argue that arti cial immune systems (AIS) exhibit the properties required by AIF, and have the potential to be exploited in the context of AIF. In this paper, we extract general features of immune systems and AIF, based on a principled meta-probe approach. We then propose an architecture for AIF incorporating ideas from AIS. Having such characteristics as adaptability, diversity and self-organised, we argue that AIS have suitable characteristics that are amenable to the task of AIF.

Original languageEnglish
Title of host publicationSoCPaR 2009 - Soft Computing and Pattern Recognition
Pages414-419
Number of pages6
DOIs
Publication statusPublished - 2009
EventInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 - Malacca, Malaysia
Duration: 04 Dec 200907 Dec 2009

Publication series

NameSoCPaR 2009 - Soft Computing and Pattern Recognition

Conference

ConferenceInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009
Country/TerritoryMalaysia
CityMalacca
Period04 Dec 200907 Dec 2009

Keywords

  • Adaptive information filtering
  • Artificial immune systems
  • Clonal selection algorithm
  • Profile adaptation

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