In this paper, we present a music recommendation system, which provides a personalized service of music recommendation. The polyphonic music objects of MIDI format are first analyzed for deriving information for music grouping. For this purpose, the representative track of each polyphonic music object is first determined, and then six features are extracted from this track for proper music grouping. Moreover, the user access histories are analyzed to derive the profiles of user interests and behaviors for user grouping. The content-based, collaborative, and statistics-based recommendation methods are proposed based on the favorite degrees of the users to the music groups, and the user groups they belong to. A series of experiments are carried out to show that our approach performs well.
Journal of Intelligent Information Systems,24:2/3,113-132