Using evolving agents to critique subjective data: Recommending music

Abstract

The authors describe a recommender model that uses intermediate agents to evaluate a large body of subjective data according to a set of rules and make recommendations to users. After scoring recommended items, agents adapt their own selection rules via interactive evolutionary computing to fit user tastes, even when user preferences undergo a rapid change. The model can be applied to such tasks as critiquing large numbers of music, image, or written compositions. In this paper we use musical selections to illustrate how agents make recommendations and report the results of several experiments designed to test the model's ability to adapt to rapidly changing conditions yet still make appropriate decisions and recommendations.