We explore the social and contextual factors that influence the outcome of
person-to-person music recommendations and discovery. Specifically, we use data
from Spotify to investigate how a link sent from one user to another results in
the receiver engaging with the music of the shared artist. We consider several
factors that may influence this process, such as the strength of the
sender-receiver relationship, the user's role in the Spotify social network,
their music social cohesion, and how similar the new artist is to the
receiver's taste. We find that the receiver of a link is more likely to engage
with a new artist when (1) they have similar music taste to the sender and the
shared track is a good fit for their taste, (2) they have a stronger and more
intimate tie with the sender, and (3) the shared artist is popular with the
receiver's connections. Finally, we use these findings to build a Random Forest
classifier to predict whether a shared music track will result in the
receiver's engagement with the shared artist. This model elucidates which type
of social and contextual features are most predictive, although peak
performance is achieved when a diverse set of features are included. These
findings provide new insights into the multifaceted mechanisms underpinning the
interplay between music discovery and social processes.

我们通过使用来自 Spotify 的数据，研究了影响人与人之间音乐推荐和发现结果的社会和环境因素，发现接收者与分享者的音乐品味相似、分享的歌曲适合接收者的口味、接收者与分享者的联系较强以及分享的艺术家受到接收者关注的程度是接收者与新艺术家互动的重要要素，并构建了一个随机森林分类器来预测分享的音乐是否会引起接收者与分享的艺术家之间的互动。这些研究结果揭示了音乐发现和社会过程之间多方面机制的新见解。

链接我再次宝贝：Spotify 上的社交音乐发现

Link Me Baby One More Time: Social Music Discovery on Spotify

As online music platforms grow, music recommender systems play a vital role
in helping users navigate and discover content within their vast musical
databases. At odds with this larger goal, is the presence of popularity bias,
which causes algorithmic systems to favor mainstream content over, potentially
more relevant, but niche items. In this work we explore the intrinsic
relationship between music discovery and popularity bias. To mitigate this
issue we propose a domain-aware, individual fairness-based approach which
addresses popularity bias in graph neural network (GNNs) based recommender
systems. Our approach uses individual fairness to reflect a ground truth
listening experience, i.e., if two songs sound similar, this similarity should
be reflected in their representations. In doing so, we facilitate meaningful
music discovery that is robust to popularity bias and grounded in the music
domain. We apply our BOOST methodology to two discovery based tasks, performing
recommendations at both the playlist level and user level. Then, we ground our
evaluation in the cold start setting, showing that our approach outperforms
existing fairness benchmarks in both performance and recommendation of
lesser-known content. Finally, our analysis explains why our proposed
methodology is a novel and promising approach to mitigating popularity bias and
improving the discovery of new and niche content in music recommender systems.

本研究旨在探索在线音乐平台上的流行度偏见问题，并提出了一种基于域感知和个体公平原则的方法来解决流行度偏见对基于图神经网络的音乐推荐系统的影响，以改善对新的和小众内容的发现。通过在冷启动设置下的评估结果表明，我们的方法在性能和推荐较不为人所知的内容方面优于现有的公平基准。