https://doi.org/10.1140/epjds/s13688-025-00531-3
Research
The impact of playlist characteristics on coherence in user-curated music playlists
1
Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz, Altenberger Straße 69, 4040, Linz, Upper Austria, Austria
2
Department of Music Pedagogy, Nuremberg University of Music, Veilhofstraße 34, 90489, Nuremberg, Bavaria, Germany
3
Human-centered AI Group, AI Lab, Linz Institute of Technology, Altenberger Straße 69, 4040, Linz, Upper Austria, Austria
Received:
6
October
2024
Accepted:
5
February
2025
Published online:
19
March
2025
Music playlist creation is a crucial, yet not fully explored task in music data mining and music information retrieval. Previous studies have largely focused on investigating diversity, popularity, and serendipity of tracks in human- or machine-generated playlists. However, the concept of playlist coherence – vaguely defined as smooth transitions between tracks – remains poorly understood and even lacks a standardized definition. In this paper, we provide a formal definition for measuring playlist coherence based on the sequential ordering of tracks, offering a more interpretable measurement compared to existing literature, and allowing for comparisons between playlists with different musical styles. The presented formal framework to measure coherence is applied to analyze a substantial dataset of user-generated playlists, examining how various playlist characteristics influence coherence. We identified four key attributes: playlist length, number of edits, track popularity, and collaborative playlist curation as potential influencing factors. Using correlation and causal inference models, the impact of these attributes on coherence across ten auditory and one metadata feature are assessed. Our findings indicate that these attributes influence playlist coherence to varying extents. Longer playlists tend to exhibit higher coherence, whereas playlists dominated by popular tracks or those extensively modified by users show reduced coherence. In contrast, collaborative playlist curation yielded mixed results. The insights from this study have practical implications for enhancing recommendation tasks, such as automatic playlist generation and continuation, beyond traditional accuracy metrics. As a demonstration of these findings, we propose a simple greedy algorithm that reorganizes playlists to align coherence with observed trends.
Key words: Music playlists / Coherence / Correlation / Causality
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-025-00531-3.
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
