Embedding-based next song recommendation for playlists

Abstract

In recent years, music storage and consumption has shifted massively to digital platforms, where large-scale libraries of songs are stored along with their metadata.As a byproduct of this transformation, music is increasingly being organized and accessed in the form of playlists.User-curated playlists have become massively available online, and the challenge of automatically generating playlists has gained popularity in the music information retrieval community.In this paper, we build on link prediction for graphs to propose a flexible music playlist generation method.We transform a playlist dataset into a weighted graph of songs and posit a Poisson model on the count of transitions between songs, where the rate is modulated by the euclidean distance between song embeddings.Our method yields prediction results superior to common deterministic baselines, suggesting that the learned embeddings can be used to derive a meaningful notion of song similarity.