Learning Latent Semantic Models for Music from Social Tags

Abstract

In this paper we describe how to build a variety of information retrieval models for music collections based on social tags. We discuss the particular nature of social tags for music and apply latent semantic dimension reduction methods to co-occurrence counts of words in tags given to individual tracks. We evaluate the performance of various latent semantic models in relation to both previous work and a simple full-rank vector space model based on tags. We investigate the extent to which our low-dimensional semantic spaces respect traditional catalogue organization by artist and genre, and how well they generalize to unseen tracks, and we illustrate some of the concepts expressed by the learned dimensions.