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Break the Loop: Gender Imbalance in Music Recommenders

Published: 14 March 2021 Publication History

Abstract

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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  • (2024)LTP-MMF: Toward Long-Term Provider Max-Min Fairness under Recommendation Feedback LoopsACM Transactions on Information Systems10.1145/369586743:1(1-29)Online publication date: 13-Sep-2024
  • (2024)Dynamic Fairness-aware Recommendation Through Multi-agent Social ChoiceACM Transactions on Recommender Systems10.1145/36906533:2(1-35)Online publication date: 28-Sep-2024
  • (2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
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cover image ACM Conferences
CHIIR '21: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval
March 2021
384 pages
ISBN:9781450380553
DOI:10.1145/3406522
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 14 March 2021

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Author Tags

  1. artists
  2. bias
  3. fairness
  4. gender balance
  5. music
  6. recommender systems

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Cited By

View all
  • (2024)LTP-MMF: Toward Long-Term Provider Max-Min Fairness under Recommendation Feedback LoopsACM Transactions on Information Systems10.1145/369586743:1(1-29)Online publication date: 13-Sep-2024
  • (2024)Dynamic Fairness-aware Recommendation Through Multi-agent Social ChoiceACM Transactions on Recommender Systems10.1145/36906533:2(1-35)Online publication date: 28-Sep-2024
  • (2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
  • (2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 21-May-2024
  • (2024)Social Choice for Heterogeneous Fairness in RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691706(1096-1101)Online publication date: 8-Oct-2024
  • (2024)It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688163(884-889)Online publication date: 8-Oct-2024
  • (2024)Fairness and Transparency in Music Recommender Systems: Improvements for ArtistsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688024(1368-1375)Online publication date: 8-Oct-2024
  • (2024)Recommend Me? Designing Fairness Metrics with ProvidersProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659044(2389-2399)Online publication date: 3-Jun-2024
  • (2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
  • (2024)To See or Not to See: Understanding the Tensions of Algorithmic Curation for Visual ArtsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658917(444-455)Online publication date: 3-Jun-2024
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