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.
| Original language | English |
|---|---|
| Title of host publication | 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 |
| Pages | 474-480 |
| Number of pages | 7 |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2007 Dec 1 |
| Event | 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 - Guangzhou, China Duration: 2006 Oct 3 → 2006 Oct 6 |
Other
| Other | 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 |
|---|---|
| Country | China |
| City | Guangzhou |
| Period | 06/10/3 → 06/10/6 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Control and Systems Engineering
Cite this
Using evolving agents to critique subjective music compositions. / Sun, Chuen Tsai; Hsieh, Ji-Lung; Huang, Chung Yuan.
2006 International Conference on Computational Intelligence and Security, ICCIAS 2006. Vol. 1 2007. p. 474-480 4072133.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Using evolving agents to critique subjective music compositions
AU - Sun, Chuen Tsai
AU - Hsieh, Ji-Lung
AU - Huang, Chung Yuan
PY - 2007/12/1
Y1 - 2007/12/1
N2 - 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.
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 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.
UR - http://www.scopus.com/inward/record.url?scp=38549152197&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38549152197&partnerID=8YFLogxK
U2 - 10.1109/ICCIAS.2006.294180
DO - 10.1109/ICCIAS.2006.294180
M3 - Conference contribution
AN - SCOPUS:38549152197
SN - 1424406056
SN - 9781424406050
VL - 1
SP - 474
EP - 480
BT - 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
ER -