Break the Loop: Gender Imbalance in Music Recommenders
Open accessAbstract
As recommender systems play an important role in everyday life, there is an increasing pressure that such systems are fair. Besides serving diverse groups of users, recommenders need to represent and serve item providers fairly as well. In interviews with music artists, we identified that gender fairness is one of the artists' main concerns. They emphasized that female artists should be given more exposure in music recommendations. We analyze a widely-used collaborative filtering approach with two public datasets-enriched with gender information-to understand how this approach performs with respect to the artists' gender. To achieve gender balance, we propose a progressive re-ranking method that is based on the insights from the interviews. For the evaluation, we rely on a simulation of feedback loops and provide an in-depth analysis using state-of-the-art performance measures and metrics concerning gen-der fairness.
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Bias often arises because the system prioritizes accuracy over fairness, favouring well-known items for recommendations without considering the satisfaction of all users.
“The bias often arises because the system prioritizes accuracy over fairness, favouring well-known items for recommendations without considering the satisfaction of all users [37].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [37]
The Related Work section says systems optimized for accuracy may privilege popular items that are not necessarily more satisfying for users. The claim strengthens this possibility into a frequent cause of bias and says the systems do not consider all users’ satisfaction, which the passage does not establish. (LLM, conf=0.83)
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Zhou, Z., Mei, K., Lu, Y. F., Wang, T., & Rao, F. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00300 · Cited by 2 · see citing works
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Verberne, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674141 · Cited by 3 · see citing works
Ferro, N., & Maistro, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674132 · Cited by 2 · see citing works
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Tolstad, I. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-64013-1_13 · Cited by 1 · see citing works
eCommerce Search and Discovery
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Linear Structure Analysis of Embeddings for Bias Disparity Reduction in Collaborative Filtering
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From Previous Plays to Long-Term Tastes
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Are Recommender Systems Serving Children? Toward Child-Aware Design and Evaluation
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Galuščáková, P., Oard, D. W., & Nair, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674136 · Cited by 2 · see citing works
Gómez, E., Cuesta, H., Gkiokas, A., Gómez-Cañón, J. S., Porcaro, L., & Yesiler, F. (2024) · ACM eBooks · doi:10.1145/3674127.3702959 · Cited by 1 · see citing works
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Algorithmic injustice and human rights
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Hacking Viral Jazz’s Gendered Hierarchies
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Music recommendation algorithms are unfair to female artists, but we can change that
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- Authors
- Ferraro, A., Serra, X., & Bauer, C.
- Affiliations
- Universitat Pompeu Fabra, Barcelona, Spain
- Universitat Pompeu Fabra (ES)
- Utrecht University (NL)
- Venue
- Proceedings of the 2021 Conference on Human Information Interaction and Retrieval
- Publisher
- ACM
- Published
- 2021-03-14
- Pages
- 249-254
- OpenAlex
- W3135583098
- Cited by
- 92
- References
- 47
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 37 · 2023: 12 · 2022: 13 · 2021: 11
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Conference on Human Information Interaction and Retrieval (publishedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Funders
- Kakao Brain
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- gender equity, music recommendation, recommender systems, women artists, gender imbalance, collaborative filtering, feedback loops, fairness metrics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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