  Playlists have become a significant part of our listening experience because
of the digital cloud-based services such as Spotify, Pandora, Apple Music.
Owing to the meteoric rise in the usage of playlists, recommending playlists is
crucial to music services today. Although there has been a lot of work done in
playlist prediction, the area of playlist representation hasn't received that
level of attention. Over the last few years, sequence-to-sequence models,
especially in the field of natural language processing, have shown the
effectiveness of learned embeddings in capturing the semantic characteristics
of sequences. We can apply similar concepts to music to learn fixed length
representations for playlists and use those representations for downstream
tasks such as playlist discovery, browsing, and recommendation. In this work,
we formulate the problem of learning a fixed-length playlist representation in
an unsupervised manner, using Sequence-to-sequence (Seq2seq) models,
interpreting playlists as sentences and songs as words. We compare our model
with two other encoding architectures for baseline comparison. We evaluate our
work using the suite of tasks commonly used for assessing sentence embeddings,
along with a few additional tasks pertaining to music, and a recommendation
task to study the traits captured by the playlist embeddings and their
effectiveness for the purpose of music recommendation.
