Community Adaptive Search Engines

Alfredo Milani*, Clement H C LEUNG, Alice Chan

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)


This paper introduces Community Adaptive Search Engines (CASE) for multimedia object retrieval. CASE systems adapt their behaviour depending on the collective feedback of the users in order to eventually converge to the optimal answer. The community adaptive approach uses continuous user feedbacks on the lists of returned objects in order to filter out irrelevant objects and promote the relevant ones. An original dealer/opponent game model for CASE is proposed and an evolutionary approach to solve the CASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain sizes.

Original languageEnglish
Pages (from-to)432-443
Number of pages12
JournalInternational Journal of Advanced Intelligence Paradigms
Issue number4
Publication statusPublished - Jun 2009

Scopus Subject Areas

  • Computer Science(all)
  • Engineering(all)
  • Applied Mathematics

User-Defined Keywords

  • Adaptive information retrieval
  • Collective knowledge
  • Evolutionary computation
  • Game theory


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