Using Evolving Agents to Critique Subjective Music Compositions

  • Chuen-Tsai Sun
  • Ji-Lung Hsieh
  • Chung-Yuan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4456)

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 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.

Keywords

Music recommender system interactive evolutionary computing adaptive agent critiquing subjective data content-based filtering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chuen-Tsai Sun
    • 1
  • Ji-Lung Hsieh
    • 1
  • Chung-Yuan Huang
    • 2
  1. 1.Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, TaiwanChina
  2. 2.Department of Computer Science and Information Engineering, Chang Gung University, 259 Wen Hwa 1st Road, Taoyuan 333, TaiwanChina

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