Link Me Baby One More Time: Social Music Discovery on Spotify
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929336722915328 |
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| author | Babul, Shazia'Ayn Hristova, Desislava Lima, Antonio Lambiotte, Renaud Beguerisse-Díaz, Mariano |
| author_facet | Babul, Shazia'Ayn Hristova, Desislava Lima, Antonio Lambiotte, Renaud Beguerisse-Díaz, Mariano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08818 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Link Me Baby One More Time: Social Music Discovery on Spotify Babul, Shazia'Ayn Hristova, Desislava Lima, Antonio Lambiotte, Renaud Beguerisse-Díaz, Mariano Social and Information Networks Information Retrieval Machine Learning Physics and Society 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. |
| title | Link Me Baby One More Time: Social Music Discovery on Spotify |
| topic | Social and Information Networks Information Retrieval Machine Learning Physics and Society |
| url | https://arxiv.org/abs/2401.08818 |