In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals' musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of their favorite artists, and foster social connections. These publicly accessible playlists transcend the boundaries of mere musical preferences: they serve as sources of rich insights into users' attributes and identities. For example, the musical preferences of elderly individuals may lean more towards Frank Sinatra, while Billie Eilish remains a favored choice among teenagers. These playlists thus become windows into the diverse and evolving facets of one's musical identity. In this work, we investigate the relationship between Spotify users' attributes and their public playlists. In particular, we focus on identifying recurring musical characteristics associated with users' individual attributes, such as demographics, habits, or personality traits. To this end, we conducted an online survey involving 739 Spotify users, yielding a dataset of 10,286 publicly shared playlists encompassing over 200,000 unique songs and 55,000 artists. Through extensive statistical analyses, we first assess a deep connection between a user's Spotify playlists and their real-life attributes. For instance, we found individuals high in openness often create playlists featuring a diverse array of artists, while female users prefer Pop and K-pop music genres. Building upon these observed associations, we create accurate predictive models for users' attributes, presenting a novel DeepSet application that outperforms baselines in most of these users' attributes.

翻译：在数字音乐流媒体时代，Spotify等平台上的播放列表已成为个人音乐体验的重要组成部分。人们创建并公开分享自己的播放列表，以表达音乐品味、推广喜爱歌手的发现，并促进社交联系。这些公开可访问的播放列表超越了单纯音乐偏好的界限：它们成为洞察用户属性和身份的丰富信息来源。例如，老年人的音乐偏好可能更倾向于Frank Sinatra，而Billie Eilish仍是青少年青睐的选择。因此，这些播放列表成为窥探个人音乐身份多样且不断演变面貌的窗口。在本研究中，我们探究了Spotify用户属性与其公开播放列表之间的关系。我们特别关注识别与用户个体属性（如人口统计特征、习惯或个性特质）相关的反复出现的音乐特征。为此，我们开展了一项涵盖739名Spotify用户的在线调查，收集了包含10,286个公开播放列表的数据集，涉及超过20万首独特歌曲和5.5万名艺术家。通过广泛的统计分析，我们首先评估了用户Spotify播放列表与其现实生活属性之间的深层关联。例如，我们发现开放性高的个体常创建涵盖多样艺术家的播放列表，而女性用户更偏好流行音乐和K-pop音乐类型。基于这些观察到的关联，我们构建了用户属性的准确预测模型，提出了一种新颖的DeepSet应用，在大多数用户属性预测任务中优于基线方法。