Using Artist Similarity to Propagate Semantic Information

Tags are useful text-based labels that encode semantic information about music (instrumentation, genres, emotions, geographic origins). While there are a number of ways to collect and generate tags, there is generally a data sparsity problem in which very few songs and artists have been accurately annotated with a sufficiently large set of relevant tags. We explore the idea of tag propagation to help alleviate the data sparsity problem. Tag propagation, originally proposed by Sordo et al., involves annotating a novel artist with tags that have been frequently associated with other similar artists. In this paper, we explore four approaches for computing artists similarity based on different sources of music information (user preference data, social tags, web documents, and audio content). We compare these approaches in terms of their ability to accurately propagate three different types of tags (genres, acoustic descriptors, social tags). We find that the approach based on collaborative filtering performs best. This is somewhat surprising considering that it is the only approach that is not explicitly based on notions of semantic similarity. We also find that tag propagation based on content-based music analysis results in relatively poor performance.