Community Adaptive Search Engines

Authors:
Alfredo Milani;Clement Leung;Alice Chan
Affiliations:
Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06100 Perugia, Italy.;Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.;Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Venue:
International Journal of Advanced Intelligence Paradigms
Year:
2009

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Abstract

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.