Representation, Exploration and Recommendation of Music Playlists (1907.01098v1)

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.

• The paper proposes a novel seq2seq framework that encodes playlists into fixed-length embeddings to capture mood, genre, and sequential order.
• The paper validates its approach by comparing seq2seq and Bag-of-Words models across tasks like genre prediction and playlist length estimation.
• The paper demonstrates that integrating playlist embeddings significantly enhances recommendation systems and fosters richer user interaction on streaming platforms.

Analysis of Playlist Representation and Recommendation via Sequence-to-Sequence Learning

The landscape of music consumption has been radically transformed by digital cloud-based services, such as Spotify and Apple Music, which have shifted user attention towards playlists. The paper "Representation, Exploration and Recommendation of Music Playlists" proposes a novel approach to playlist representation as a method to enhance the recommendation process. Drawing inspiration from NLP, specifically sequence-to-sequence (Seq2seq) learning, the authors focus on the unsupervised learning of playlist embeddings as a basis for more efficient recommendation systems.

The Motivation for Playlist Embeddings

The traditional focus in playlist recommendation has been on automatic playlist generation and continuation. However, playlist discovery and representation have been comparatively overlooked. Utilizing fixed-length embeddings presents an opportunity to compactly encapsulate semantic properties, such as mood and genre, vastly improving recommendation efficiency and broadening discovery capabilities.

The paper suggests using Seq2seq models to embed playlists similarly to sentences in NLP. Songs in a playlist are treated analogously to words in a sentence, thus allowing the extraction of nuanced, semantically rich playlist embeddings that facilitate a variety of tasks, including direct recommendations.

Methodological Overview

The core of the research employs Seq2seq models alongside Bag-of-Words (BoW) models for comparison. The models' performances were evaluated across several tasks derived from NLP, such as genre prediction and playlist length prediction. The Seq2seq model architecture leverages RNNs with LSTM units, enhanced by the attention mechanism to overcome the challenge of capturing long-term dependencies in playlists.

Corpus and Dataset: Source data comprises a substantial collection of playlists obtained through Spotify’s API, where specific filtering and clustering techniques (such as word2vec for song embeddings) reduce genre ambiguity.

Experimental Analysis

The paper’s experimental setup revolves around two primary evaluation components: embedding probing and recommendation tasks. The evaluation framework tests the models' abilities to capture and leverage playlist characteristics for recommendation purposes.

1. Embedding Probing: Tasks assess how well models embed genre, playlist length, and content. Results indicate BoW-based models excel in genre association, likely due to their simplistic aggregate nature favoring direct genre-embedding alignment. Conversely, the Seq2seq models encapsulate playlist length and order information more effectively, reflected in their superior ability to discern order-specific traits like song sequence.

2. Recommendation Task: Evaluating playlist embeddings in a recommendation context reveals that Seq2seq models perform better concerning song order and length properties. This aligns with the Seq2seq's robust handling of sequential data and contextual nuances — critical for predicting playlist continuation.

Implications and Future Directions

The authors highlight the potential for playlist embeddings to propel the development of more intuitive, efficiency-driven recommendation systems. These findings advocate for an ensemble of BoW and Seq2seq models to capitalize on the strengths of each method—BoW's genre capture and Seq2seq's order sensitivity. Moreover, integrating additional song-related content information (e.g., lyrics, audio features) could further enhance the semantic learning, facilitating better unseen playlist incorporation.

The exploration of playlist representations via Seq2seq models lays foundational work for richer, more dynamic interaction between users and music streaming platforms. This intersection of NLP methodologies with music recommendation opens avenues for deeper, personalized content curation, providing a pivotal basis for future research focused on embedding-based recommendation systems.