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.