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.

在数字音乐流媒体时代，Spotify 平台上的播放列表已成为个人音乐体验的重要组成部分，本文研究了 Spotify 用户的属性与其公开播放列表之间的关系，并创建了准确的预测模型。

从公开的 Spotify 播放列表中挖掘用户属性

"All of Me": Mining Users' Attributes from their Public Spotify  Playlists

Personalization of playlists is a common feature in music streaming services,
but conventional techniques, such as collaborative filtering, rely on explicit
assumptions regarding content quality to learn how to make recommendations.
Such assumptions often result in misalignment between offline model objectives
and online user satisfaction metrics. In this paper, we present a reinforcement
learning framework that solves for such limitations by directly optimizing for
user satisfaction metrics via the use of a simulated playlist-generation
environment. Using this simulator we develop and train a modified Deep
Q-Network, the action head DQN (AH-DQN), in a manner that addresses the
challenges imposed by the large state and action space of our RL formulation.
The resulting policy is capable of making recommendations from large and
dynamic sets of candidate items with the expectation of maximizing consumption
metrics. We analyze and evaluate agents offline via simulations that use
environment models trained on both public and proprietary streaming datasets.
We show how these agents lead to better user-satisfaction metrics compared to
baseline methods during online A/B tests. Finally, we demonstrate that
performance assessments produced from our simulator are strongly correlated
with observed online metric results.

使用强化学习技术，在模拟的歌单生成环境中直接优化用户满意度指标，从而实现对大规模和动态候选歌曲集的个性化推荐。通过离线模拟和在线 A/B 测试验证，该方法相对于基线方法在用户满意度指标上表现更好，并且通过模拟器得到的性能评估与在线指标结果强相关。