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

• URL: http://arxiv.org/abs/2409.03781v1
• Date: Thu, 22 Aug 2024 11:44:46 GMT
• Title: It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
• Authors: Andres Ferraro, Michael D. Ekstrand, Christine Bauer,
• Abstract summary: We 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.
• Score: 7.94306624344211
• License:
• 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.

Related papers

• Understanding and Addressing Gender Bias in Expert Finding Task [6.239590365208578]
  This study investigates gender bias in state-of-the-art Expert Finding (EF) models. Our findings reveal that models relying on reputation metrics and activity levels disproportionately favor male users. We propose adjustments to EF models that incorporate a more balanced preprocessing strategy and leverage content-based and social network-based information.
  arXiv  Detail & Related papers  (2024-07-07T11:35:23Z)
• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems [82.92678837778358]
  preference-based methods have demonstrated substantial success in empirical applications such as InstructGPT. We show how human bias and uncertainty in feedback modelings can affect the theoretical guarantees of these approaches.
  arXiv  Detail & Related papers  (2023-07-24T17:50:24Z)
• Improving Recommendation Fairness via Data Augmentation [66.4071365614835]
  Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes. In this paper, we study how to improve recommendation fairness from the data augmentation perspective.
  arXiv  Detail & Related papers  (2023-02-13T13:11:46Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness [6.210698627561645]
  We investigate how recommender performance varies according to popularity and demographics. We find statistically significant differences in recommender performance by both age and gender. We observe that recommendation utility steadily degrades for older users, and is lower for women than men.
  arXiv  Detail & Related papers  (2021-10-15T20:30:51Z)
• Correcting the User Feedback-Loop Bias for Recommendation Systems [34.44834423714441]
  We propose a systematic and dynamic way to correct user feedback-loop bias in recommendation systems. Our method includes a deep-learning component to learn each user's dynamic rating history embedding. We empirically validated the existence of such user feedback-loop bias in real world recommendation systems.
  arXiv  Detail & Related papers  (2021-09-13T15:02:55Z)
• Bias: Friend or Foe? User Acceptance of Gender Stereotypes in Automated Career Recommendations [8.44485053836748]
  We show that a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using career recommendation as a case study, we build a fair AI career recommender by employing gender debiasing machine learning techniques.
  arXiv  Detail & Related papers  (2021-06-13T23:27:45Z)
• Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback [62.997667081978825]
  We present a novel approach for considering user feedback and evaluate it using three distinct strategies. Despite a limited number of feedbacks returned by users (as low as 20% of the total), our approach obtains similar results to those of state of the art approaches.
  arXiv  Detail & Related papers  (2020-09-16T07:32:51Z)
• Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
  We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
  arXiv  Detail & Related papers  (2020-06-03T05:04:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.