@inproceedings{f6bbc9371c494b04b7d531520eb2286d,
title = "Evaluating subjective compositions by the cooperation between human and adaptive agents",
abstract = "We describe a music recommender model that uses intermediate agents to evaluate music composition according to their own rules respectively, and make recommendations to user. After user scoring recommended items, agents can adapt their selection rules to fit user tastes, even when user preferences undergo a rapid change. Depending on the number of users, the model can also be applied to such tasks as critiquing large numbers of music, image, or written compositions in a competitive contest with other judges. Several experiments are reported to test the model's ability to adapt to rapidly changing conditions yet still make appropriate decisions and recommendations.",
keywords = "Adaptive agent, Content-based filtering, Critiquing subjective data, Interactive evolutionary computing, Music recommender system",
author = "Huang, {Chung Yuan} and Hsieh, {Ji Lung} and Sun, {Chuen Tsai} and Cheng, {Chia Ying}",
year = "2006",
doi = "10.1007/11925231_93",
language = "English",
isbn = "3540490264",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "974--984",
booktitle = "MICAI 2006",
note = "5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence ; Conference date: 13-11-2006 Through 17-11-2006",
}