Intelligent Music Playlist Recommendation Based on User Daily Behavior and Music Content

摘要

A music hobbyist listens to different types of music at different times of the day. Thus, an automatic music recommendation that can adjust to the hobbyist's daily activities on this basis is necessary in order to generate the appropriate music to suit the user's current activity, whether it is working or driving. Although existing research has introduced various music recommendation systems, there is yet a system that generates the music recommendation based on time. Hence, in this paper, we present a music recommendation system, which provides an automatic and personalized music playing service based on the time parameter and user's interesting. This system represents the characteristics of music from features extracted out of the music's symbolic form. The user's music rating history and the associated time stamps in the user's profile constitute the training data of the intelligent system. The effectiveness and efficiency of artificial neural network and decision tree are investigated as the kernels of the system. A series of experiments have been carried out to demonstrate the performance of this system.