Heterogeneous embedding for subjective artist similarity

Abstract

We describe an artist recommendation system which inte-grates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic fea-tures, we learn a low-dimensional feature transformation which is optimized to reproduce human-derived measure-ments of subjective similarity between artists. By produc-ing low-dimensional representations of artists, our system is suitable for visualization and recommendation tasks. 1