用路径元分析用户听歌口味变化，发现可解释的听歌模式。
Modeling Musical Genre Trajectories through Pathlet Learning
- 提出路径元学习框架，捕捉用户跨流派的重复听歌模式。
- 基于2000名用户17个月数据，识别出可解释的听歌轨迹模式。
- 适合研究推荐系统多样性与用户行为分析的学者使用。
音乐流媒体平台用户数据的日益丰富为分析音乐消费提供了新机遇。然而，理解用户偏好随时间演变仍具挑战性。本文采用字典学习范式，建模用户在不同音乐流派中的行为轨迹。提出一种新框架，通过定义称为'路径元'的重复模式，生成可解释的轨迹嵌入表示。实验表明，路径元学习能揭示有意义的听歌模式，支持定性与定量分析。该研究深化了对用户与音乐互动的理解，为推荐系统中用户行为研究及多样性促进开辟新路径。研究使用由领先音乐流媒体公司Deezer提供的数据集，包含2000名用户历时17个月的、按流派标注的听歌历史，并开源代码。
原文摘要 · Abstract (English)
The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user preferences remains a complex challenge, particularly as their musical tastes change over time. This paper uses the dictionary learning paradigm to model user trajectories across different musical genres. We define a new framework that captures recurring patterns in genre trajectories, called pathlets, enabling the creation of comprehensible trajectory embeddings. We show that pathlet learning reveals relevant listening patterns that can be analyzed both qualitatively and quantitatively. This work improves our understanding of users' interactions with music and opens up avenues of research into user behavior and fostering diversity in recommender systems. A dataset of 2000 user histories tagged by genre over 17 months, supplied by Deezer (a leading music streaming company), is also released with the code.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。