Using evolving agents to critique subjective data: Recommending music

Ji Lung Hsieh; Chuen Tsai Sun; Chung Yuan Huang

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 proceeding › Conference contribution › peer-review

2 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 language	English
Title of host publication	2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages	406-413
Number of pages	8
State	Published - 2006
Externally published	Yes
Event	2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada Duration: 16 07 2006 → 21 07 2006

Publication series

Name	2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

Conference	2006 IEEE Congress on Evolutionary Computation, CEC 2006
Country/Territory	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

Cite this

@inproceedings{ca418deb184945c5899377efcc15ef99,

title = "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.",

author = "Hsieh, {Ji Lung} and Sun, {Chuen Tsai} and Huang, {Chung Yuan}",

year = "2006",

language = "英语",

isbn = "0780394879",

series = "2006 IEEE Congress on Evolutionary Computation, CEC 2006",

pages = "406--413",

booktitle = "2006 IEEE Congress on Evolutionary Computation, CEC 2006",

note = "2006 IEEE Congress on Evolutionary Computation, CEC 2006 ; Conference date: 16-07-2006 Through 21-07-2006",

}

Using evolving agents to critique subjective data: Recommending music. / Hsieh, Ji Lung; Sun, Chuen Tsai; Huang, Chung Yuan.
2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 406-413 1688337 (2006 IEEE Congress on Evolutionary Computation, CEC 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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M3 - 会议稿件

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ER -

Using evolving agents to critique subjective data: Recommending music

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