Using evolving agents to critique subjective data: Recommending music

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages406-413
Number of pages8
StatePublished - 1 Dec 2006
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

Name2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

Conference2006 IEEE Congress on Evolutionary Computation, CEC 2006
CountryCanada
CityVancouver, BC
Period16/07/0621/07/06

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    Hsieh, J. L., Sun, C-T., & Huang, C. Y. (2006). Using evolving agents to critique subjective data: Recommending music. In 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 406-413). [1688337] (2006 IEEE Congress on Evolutionary Computation, CEC 2006).