Abstract Recommendation systems frequently exhibit two fundamental types of bias: user-side bias, where recommendations discriminate based on protected attributes such as gender, and item-side bias, where popular items dominate exposure irrespective of their actual relevance or quality. To address both simultaneously, we propose a dual-adversarial framework with two discriminators: a user-gender discriminator designed to mitigate gender bias by removing sensitive information from user embeddings, and an item-popularity discriminator aimed at reducing popularity bias among items. While adversarial learning encourages the model to generate bias-free embeddings by fooling discriminators, residual sensitive information may persist due to imperfect discriminator inference and distributional discrepancies. To further reduce this residual bias and enforce balanced, fair recommendations, we enhance the dual adversarial learning framework with two explicit fairness constraints that operate on the learned debiased user and item embeddings: an individual user fairness constraint that guarantees equitable recommendations for users with similar preferences, and a popularity regularization term that balances exposure across popular and less popular item groups. Experiments on three real-world datasets (MovieLens 1M, MovieLens 100K, and Book-Crossing) demonstrate significant gender predictability drops and popularity skew decreases, while maintaining competitive recommendation quality.
Karboua et al. (Thu,) studied this question.