Representation, Exploration, and Recommendation of Music Playlists

Abstract

Playlists have become a significant part of our listening experience because of digital cloud-based services such as Spotify, Pandora, Apple Music, making playlist recommendation crucial to music services today. With an aim towards playlist discovery and recommendation, we leverage sequence-to-sequence modeling to learn a fixed-length representation of playlists in an unsupervised manner. We evaluate our work using a recommendation task, along with embedding-evaluation tasks, to study the extent to which semantic characteristics such as genre, song-order, etc. are captured by the playlist embeddings and how they can be leveraged for music recommendation.