Learning to embed songs and tags for playlist prediction

Joshua L. Moore, Cornell University
Shuo Chen, Cornell University
Thorsten Joachims, Cornell University
Douglas Turnbull, Ithaca College


Automatically generated playlists have become an important medium for accessing and exploring large collections of music. In this paper, we present a probabilistic model for generating coherent playlists by embedding songs and social tags in a unified metric space. We show how the embedding can be learned from example playlists, providing the metric space with a probabilistic meaning for song/song, song/tag, and tag/tag distances. This enables at least three types of inference. First, our models can generate new playlists, outperforming conventional n-gram models in terms of predictive likelihood by orders of magnitude. Second, the learned tag embeddings provide a generalizing representation for embedding new songs, allowing it to create playlists even for songs it has never observed in training. Third, we show that the embedding space provides an effective metric for matching songs to natural-language queries, even if tags for a large fraction of the songs are missing. © 2012 International Society for Music Information Retrieval.