Adaptive search engines as discovery games: An evolutionary approach

Adaptive search engines (ASE), used in the retrieval of multimedia objects adapt their behavior depending on the user feedback in order to eventually converge to the optimal answer. The adaptive architecture has been shown to improve the performance in case of multimedia objects retrieval, when pre-indexing techniques are costly or can be applied only partially. The continuous user feedbacks onthe lists of returned objects are used to filter out irrelevant objects and promote the relevant ones. This work propose an original dealer/opponent game model for ASE. The system/user interactive process which takes place in ASE can be modeled as a discovery game between a dealer, the user community which holds a secret consisting in the optimal answer to a query, and an opponent, i.e. the system, which tries to discover the secret by submitting tentative solutions on which it receives the user/dealer feedback. It is shown how the complexity of the game can be related to known games. An evolutionary approach to solve the ASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain size. The proposed schema is quite general and can fit other adaptive search architectures which appear in ebusiness and e-commerce applications.