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.
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Huang, CY., Hsieh, JL., Sun, CT., Cheng, CY. (2006). Evaluating Subjective Compositions by the Cooperation Between Human and Adaptive Agents. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_93
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DOI: https://doi.org/10.1007/11925231_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
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