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播放列表特征对用户创建的音乐播放列表连贯性的影响。

The impact of playlist characteristics on coherence in user-curated music playlists.

作者信息

Schweiger Harald, Parada-Cabaleiro Emilia, Schedl Markus

机构信息

Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz, Altenberger Straße 69, Linz, 4040 Upper Austria Austria.

Department of Music Pedagogy, Nuremberg University of Music, Veilhofstraße 34, Nuremberg, 90489 Bavaria Germany.

出版信息

EPJ Data Sci. 2025;14(1):24. doi: 10.1140/epjds/s13688-025-00531-3. Epub 2025 Mar 19.

Abstract

UNLABELLED

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.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-025-00531-3.

摘要

未标注

音乐播放列表创建是音乐数据挖掘和音乐信息检索中一项至关重要但尚未得到充分探索的任务。以往的研究主要集中在调查人工或机器生成的播放列表中曲目的多样性、受欢迎程度和意外发现。然而,播放列表连贯性的概念——大致定义为曲目之间的平滑过渡——仍然理解不足,甚至缺乏标准化的定义。在本文中,我们基于曲目的顺序排列为测量播放列表连贯性提供了一个正式定义,与现有文献相比,提供了更具可解释性的测量方法,并允许对不同音乐风格的播放列表进行比较。所提出的用于测量连贯性的正式框架被应用于分析大量用户生成的播放列表数据集,研究各种播放列表特征如何影响连贯性。我们确定了四个关键属性:播放列表长度、编辑次数、曲目受欢迎程度和协作式播放列表策划作为潜在影响因素。使用相关性和因果推断模型,评估了这些属性对十个听觉特征和一个元数据特征的连贯性的影响。我们的研究结果表明,这些属性在不同程度上影响播放列表的连贯性。较长的播放列表往往表现出更高的连贯性,而以热门曲目为主或被用户大量修改的播放列表则表现出较低的连贯性。相比之下,协作式播放列表策划产生了混合结果。这项研究的见解对于超越传统准确性指标来增强推荐任务(如自动播放列表生成和延续)具有实际意义。作为这些发现的一个例证,我们提出了一种简单的贪心算法,该算法重新组织播放列表,使其连贯性与观察到的趋势保持一致。

补充信息

在线版本包含可在10.1140/epjds/s13688-025-00531-3获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61ec/11923031/11d3a00af143/13688_2025_531_Fig1_HTML.jpg

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