Social Knowledge-Driven Music Hit Prediction

What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches.