A Stakeholder-Centered View on Fairness in Music Recommender Systems
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Abstract (arXiv)
Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably impacts both the users of music streaming platforms and the artists providing music to those platforms. The distinct needs that these stakeholder groups may have, and the different aspects of fairness that therefore should be considered, make for a challenging research field with ample opportunities for improvement. The review first outlines current literature on MRS fairness from the perspective of each stakeholder and the stakeholders combined, and then identifies promising directions for future research. The two open questions arising from the review are as follows: (i) In the MRS field, only limited data is publicly available to conduct fairness research; most datasets either originate from the same source or are proprietary (and, thus, not widely accessible). How can we address this limited data availability? (ii) Overall, the review shows that the large majority of works analyze the current situation of MRS fairness, whereas only few works propose approaches to improve it. How can we move forward to a focus on improving fairness aspects in these recommender systems? At FAccTRec '22, we emphasize the specifics of addressing RS fairness in the music domain.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Sep 2022.
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Citing papers per year: 2024: 3; 2025: 2.
5 papers.
- 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
- 2024Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Datacited by 2
- 2024SoundSignature: What Type of Music Do You Like?cited by 2
- 2024Fair Augmentation for Graph Collaborative Filteringcited by 1
- 2025Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluationcited by 1
- 2025Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation20 Nov 2025
- 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?20 Aug 2025
- 2024SoundSignature: What Type of Music Do You Like?4 Oct 2024
- 2024Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data29 Aug 2024
- 2024Fair Augmentation for Graph Collaborative Filtering22 Aug 2024