Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems
This work addresses fairness issues in recommendation systems for users and practitioners, but it is incremental as it builds on existing re-ranking methods.
The paper tackles bias in recommendation systems by proposing a fairness-aware re-ranking approach that addresses biases across multiple item categories and sensitive attributes like gender, age, and occupation, showing it mitigates social bias with minimal performance degradation on three real-world datasets.
Recommendation systems play a crucial role in our daily lives by impacting user experience across various domains, including e-commerce, job advertisements, entertainment, etc. Given the vital role of such systems in our lives, practitioners must ensure they do not produce unfair and imbalanced recommendations. Previous work addressing bias in recommendations overlooked bias in certain item categories, potentially leaving some biases unaddressed. Additionally, most previous work on fair re-ranking focused on binary-sensitive attributes. In this paper, we address these issues by proposing a fairness-aware re-ranking approach that helps mitigate bias in different categories of items. This re-ranking approach leverages existing biases to correct disparities in recommendations across various demographic groups. We show how our approach can mitigate bias on multiple sensitive attributes, including gender, age, and occupation. We experimented on three real-world datasets to evaluate the effectiveness of our re-ranking scheme in mitigating bias in recommendations. Our results show how this approach helps mitigate social bias with little to no degradation in performance.