A personalized music recommendation system with a time-weighted clustering

We propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a pieces of music is mapped into a point in the property space and the time is converted into the weight of the point. The more recently the user listens to the music, the more the weight increases. We apply the K-means clustering algorithm to the weighted points. The K-means algorithm is modified so that the number of clusters are dynamically changed. 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 also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. In this paper we present and evaluate algorithms to recommend system.