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.

翻译：我们探索了影响人际音乐推荐与发现结果的社会及情境因素。具体而言，我们利用Spotify的数据研究用户之间发送的链接如何促使接收者关联所分享艺术家的音乐。我们考虑了可能影响这一过程的若干因素，例如发送者与接收者之间的关系强度、用户在Spotify社交网络中的角色、他们的音乐社交凝聚力，以及新艺术家与接收者音乐品味的相似程度。我们发现，当满足以下条件时，链接接收者更有可能与新的艺术家互动：（1）接收者与发送者拥有相似的音乐品味，且分享的曲目契合其喜好；（2）接收者与发送者之间关系更强、更亲密；（3）分享的艺术家在接收者的社交圈中受欢迎。最后，我们利用这些发现构建了一个随机森林分类器，用于预测分享的音乐曲目是否能促使接收者与所分享的艺术家产生互动。该模型揭示了哪些社会及情境特征最具预测力，不过当包含多样化的特征组合时能达到最佳性能。这些发现为理解音乐发现与社会过程之间相互作用的多重机制提供了新的见解。