Logo image
Back
It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
Conference proceeding   Open access

It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation

Andres Ferraro, Michael D. Ekstrand and Christine Bauer
Proceedings of the 18th ACM Conference on Recommender Systems, pp 884-889
08 Oct 2024
DOI: https://doi.org/10.1145/3640457.3688163
url
https://doi.org/10.1145/3640457.3688163View
Published, Version of Record (VoR) Open CC BY-NC-ND V4.0

Abstract

Human-centered computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Collaborative filtering Human-centered computing -- Collaborative and social computing -- Empirical studies in collaborative and social computing Information systems -- Information retrieval -- Retrieval tasks and goals -- Recommender systems Social and professional topics -- User characteristics -- Gender
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.

Metrics

11 Record Views
12 citations in Web of Science
3 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Logo image