The artists' perspective on music recommender systems

Idea

Music recommender systems are used in streaming services (e.g., Spotify, YouTube); they recommend music to users based on their musical taste, listening activity, or mood. Some examples of recommender systems are automatic playlist generation (e.g., Spotify’s personalized ‘Discover Weekly’ or unpersonalized ‘Popular Music from the 90s’), suggestions to add tracks to an existing playlist, or artists suggested based on your previous listening behavior. This project aims to identify potential problems that affect artists and draw on your ideas about how you would like future recommender systems to work. 

Relevant references:

• Andrés Ferraro, Xavier Serra, & Christine Bauer (2021). What is fair? Exploring the artists’ perspective on the fairness of music streaming platforms. In Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda et al. (Eds.), Human-Computer Interaction – INTERACT 2021. Volume 12933, pp 562-584. Cham, Germany: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-85616-8_33
• Karlijn Dinnissen & Christine Bauer (2023). How control and transparency for users could improve artist fairness in music recommender systems. Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR 2023). Milan, Italy, 5-9 November, Art no. 57, pp 482-491. DOI: https://doi.org/10.5281/zenodo.10265331
• Karlijn Dinnissen & Christine Bauer (2023). Amplifying artists’ voices: item provider perspectives on influence and fairness of music streaming platforms. Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2023). Limassol, Cyprus, 26-29 June, pp 238-249. DOI: https://doi.org/10.1145/3565472.3592960
• Christine Bauer (2020). Report on the ISMIR 2020 special session: how do we help artists? ACM SIGIR Forum, 54(2), Art no. 13, pp 1-7. DOI: https://doi.org/10.1145/3483382.3483398