||With the music signal processing and network development, people can listen to music not only CDs, radio but also mobile devices, even more, online streaming.|
Music recommendation systems use audio signals as classification features to predict user prefferences.In the recent years, music metadata such as music genre, music tags, and playlists can be treated as attribute to build recommendation models. The music database is growing explosively so that it is hard for users to find exactly what they want. Nowadays, there are many music services have recommendation systems for their users to find out this potential ‘songs’ quickly.
In online streaming environment, this research considers user playlists as user preferences, and converts playcounts to user preference rates. By this step, rates play a role in the classification and collaborative filtering methods. In music information retrieval field, content-based(audio, music tags) and collaborative approach are well known and frequently implemented in music services. In this research, content-based approach uses music signals and tag as classification features, and tries to combine both attributes into classification model; on the other hand, collaborative approach, uses k-nearest neighborhood method and matrix decomposition by tuning thresholds and vector weights. Both of these approachs are focusing on music items which user inetersted. According to the experement, approached methods accuracies are higher than traditional ones.