標題: Using evolving agents to critique subjective data: Recommending music
作者: Hsieh, Ji-Lung
Sun, Chuen-Tsai
Huang, Chung-Yuan
資訊工程學系
Department of Computer Science
公開日期: 2006
摘要: 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.
URI: http://hdl.handle.net/11536/17193
ISBN: 978-0-7803-9487-2
期刊: 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6
起始頁: 406
結束頁: 413
顯示於類別:會議論文