Music recommendation using graph based quality model

Nowadays, listening to music has become a habit for almost everyone. Music recommendation helps the users discover the songs they like to listen. In this paper, we propose a music recommendation framework based on graph based quality model to make fine-grained music recommendation. We first discover the recommendation cues, which we called user׳s preference relations from the users' ratings. Then we model them using the quality model and propose a regularization framework to calculate the recommendation probability of songs. Our experiments show that the proposed framework is superior to two traditional algorithms, especially in solving the cold start problem in recommendation.