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

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.

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Institutions

Publication Details

Journal
Proceedings of the International AAAI Conference on Web and Social Media
Published
2025-06-07
DOI
https://doi.org/10.1609/icwsm.v19i1.35808
Citations
2
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
2.97

Funders

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article

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

Mariano Beguerisse-Díaz, Renaud Lambiotte, Desislava Hristova, Antônio Adílson Soares de Lima et al.
2 citations
Proceedings of the International AAAI Conference on Web and Social Media
Music and Audio Processing
2.97
article

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

Mariano Beguerisse-Díaz, Renaud Lambiotte, Desislava Hristova, Antônio Adílson Soares de Lima, Shazia'Ayn Babul
article en
2 citations

Abstract

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.

Proceedings of the International AAAI Conference on Web and Social MediaVol. 19
Turing Institute (GB), University of Oxford (GB), The Alan Turing Institute (GB)
Alan Turing Institute, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 9%
Music and Audio Processing
2.97
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