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 amongst 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：我们探讨了影响人与人之间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，研究从一个用户发送给另一个用户的链接如何导致接收者关注共享艺术家的音乐。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关系的强度、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度。研究发现，当（1）接收者与发送者具有相似的音乐品味，且共享曲目与接收者品味高度契合，（2）接收者与发送者之间存在更强、更亲密的关系，以及（3）共享艺术家在接收者的社交圈中受欢迎时，接收者更可能关注新艺术家。最后，我们利用这些发现构建了一个随机森林分类器，以预测共享音乐曲目是否会导致接收者关注共享艺术家。该模型揭示了哪些类型的社交和情境特征最具预测性，尽管当包含多样化特征时才能达到最佳性能。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见解。