# Representation, Exploration and Recommendation of Music Playlists

## TL;DR

This paper introduces an unsupervised sequence-to-sequence approach to learn fixed-length playlist representations, enabling improved playlist discovery, browsing, and recommendation in music streaming services.

## Contribution

It applies Seq2seq models to music playlists for the first time, learning semantic embeddings that enhance downstream music recommendation and exploration tasks.

## Key findings

- Playlist embeddings outperform baseline models in semantic tasks
- Embeddings improve accuracy of playlist recommendation
- Sequence-to-sequence models effectively capture playlist semantics

## Abstract

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.