Using evolving agents to critique subjective data: Recommending music

Ji Lung Hsieh*, Chuen Tsai Sun, Chung Yuan Huang

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

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.

原文英語
主出版物標題2006 IEEE Congress on Evolutionary Computation, CEC 2006
頁面406-413
頁數8
出版狀態已發佈 - 2006
事件2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, 加拿大
持續時間: 2006 7月 162006 7月 21

出版系列

名字2006 IEEE Congress on Evolutionary Computation, CEC 2006

其他

其他2006 IEEE Congress on Evolutionary Computation, CEC 2006
國家/地區加拿大
城市Vancouver, BC
期間2006/07/162006/07/21

ASJC Scopus subject areas

  • 人工智慧
  • 軟體
  • 理論電腦科學

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