Remixing “Taste”

Authorship and Attribution in Spotify Blend Playlists


  • Morghen Jael Faculty of Information, University of Toronto



Spotify Blend, digital music playlists, remix, mashup, authorship, attribution, collaboration


In this paper, I discuss dimensions of remix, including attribution and authorship, for automated digital music playlists. I use the case study of Spotify Blend, an automated, personalized, mock-collaborative playlist feature that combines up to ten users’ music taste and listening histories and regenerates its content daily. I defend the characterization of Spotify Blend as an example of “remix” (or an example of “mashup,” a related concept), wherein the source material being remixed is user listening data and wherein sampling is the primary remix tactic. In fleshing out this characterization, I discuss how the concepts of “authorship” and “attribution” operate in the context of Spotify Blend, with the important acknowledgment that Spotify’s algorithm remains opaque to users. I also compare Spotify Blend with user-generated, actively collaborative playlists created on the same platform. Observations about Blend and its features of use derive mostly from personal experience with the program.




How to Cite

Jael, M. (2023). Remixing “Taste”: Authorship and Attribution in Spotify Blend Playlists. Pathfinder: A Canadian Journal for Information Science Students and Early Career Professionals, 4(1), 28–43.



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