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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
ArXiv.org
22 Aug 2024
Abstract
As recommender systems are prone to various biases, mitigation approaches are
needed to ensure that recommendations are fair to various stakeholders. One
particular concern in music recommendation is artist gender fairness. Recent
work has shown that the gender imbalance in the sector translates to the output
of music recommender systems, creating a feedback loop that can reinforce
gender biases over time. In this work, we examine that feedback loop to study
whether algorithmic strategies or user behavior are a greater contributor to
ongoing improvement (or loss) in fairness as models are repeatedly re-trained
on new user feedback data. We simulate user interaction and re-training to
investigate the effects of ranking strategies and user choice models on gender
fairness metrics. We find re-ranking strategies have a greater effect than user
choice models on recommendation fairness over time.
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Details
- Title
- It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
- Creators
- Andres FerraroMichael D EkstrandChristine Bauer
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Other Identifier
- 991021903401104721