Music recommendation system based on usage history and automatic genre classification

The personalized music recommender supports the user-favorite songs stored in a huge music database. In order to predict only user-favorite songs, managing user preferences information and genre classification are necessary. 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. We propose the system about the automatic management of the user preferences and genre classification in the personalized music system.