Understanding Music Playlists

2015/11/22 by Keunwoo Choi, Choi, Keunwoo, George Fazekas +3 · 1 citation
Computer Science · Neuroscience · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Multimedia (cs.MM) #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception

paper · pdf · doi:10.48550/arxiv.1511.07004

openalex publication_date 2015/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

As music streaming services dominate the music industry, the playlist is becoming an increasingly crucial element of music consumption. Con- sequently, the music recommendation problem is often casted as a playlist generation prob- lem. Better understanding of the playlist is there- fore necessary for developing better playlist gen- eration algorithms. In this work, we analyse two playlist datasets to investigate some com- monly assumed hypotheses about playlists. Our findings indicate that deeper understanding of playlists is needed to provide better prior infor- mation and improve machine learning algorithms in the design of recommendation systems.

Cited by

• Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

Related

• Machine Learning Approaches to Hybrid Music Recommender Systems
• Quick Lists: Enriched Playlist Embeddings for Future Playlist\n Recommendation
• The Importance of Song Context and Song Order in Automated Music\n Playlist Generation
• Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs
• The Potential of the Confluence of Theoretical and Algorithmic Modeling in Music Recommendation