A music recommendation system with a dynamic K-means clustering algorithm

A large number of people download music files easily from web sites. But rare music sites provide personalized services. So, we suggest a method for personalized services. We extract the properties of music from music's sound wave. We use STFT (Shortest Time Fourier Form) to analyze music's property. And we infer users' preferences from users' music list. To analyze users' preferences we propose a dynamic K-means clustering algorithm. The dynamic K-means clustering algorithm clusters the pieces in the music list dynamically adapting the number of clusters. We recommend pieces of music based on the clusters. The previous recommendation systems analyze a user's preference by simply averaging the properties of music in the user's list. So those cannot recommend correctly if a user prefers several genres of music. By using our K-means clustering algorithm, we can recommend pieces of music which are close to user's preference even though he likes several genres. We perform experiments with one hundred pieces of music. In this paper we present and evaluate algorithms to recommend music.