期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：5
出版社：Journal of Theoretical and Applied
摘要：Background/Objectives: The personalized music recommendation services can support the user-favorite contents among various multimedia contents. In order to predict user-favorite songs, it is necessary to manage user preferences information and genre classification. Methods/Statistical analysis: We introduce the mechanism about the automatic management of the user preferences in the personalized music recommendation service. This system automatically extracts the user preference data from the user�s brain waves and audio features from music. Findings: In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. We applied a distance metric learning algorithm in order to reduce the dimensionality of feature vector with a little performance degradation. Proposed user�s preference classifier achieved an overall accuracy of 81.07% in the binary preference classification for the KETI AFA2000 music corpus. Improvements/Applications: we could recognize the user�s satisfaction when we use brainwaves. This system can be applied to various audio devices, apps and services.
