Department of Computer Science
Community adaptive search engines
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. Copyright © 2009 Inderscience Enterprises Ltd.
Adaptive information retrieval, Collective knowledge, Evolutionary computation, Game theory
Source Publication Title
International Journal of Advanced Intelligence Paradigms
Link to Publisher's Edition
Milani, Alfredo, Clement Leung, and Alice Chan. "Community adaptive search engines." International Journal of Advanced Intelligence Paradigms 1.4 (2009): 432-443.