A personalized music filtering system based on melody style classification

With the growth of digital music, the personalized music filtering system is helpful for users. Melody style is one of the music features to represent user´s music preference. We present a personalized content-based music filtering system to support music recommendation based on user´s preference of melody style. We propose the multitype melody style classification approach to recommend the music objects. The system learns the user preference by mining the melody patterns from the music access behavior of the user. A two-way melody preference classifier is therefore constructed for each user. Music recommendation is made through this melody preference classifier. Performance evaluation shows that the filtering effect of the proposed approach meets user´s preference.