Enhancing User-Side Fairness on Neural Collaborative Filtering Using Generative Adversarial Network-Based Augmentation

Abstract

Deep recommender systems frequently suffer from algorithmic biases that lead to unequal recommendation exposure across demographic groups, particularly disadvantaging underrepresented users. Although various debiasing methods have been proposed, they typically rely on invasive architectural adjustments that disrupt the core algorithm, causing a severe trade-off between fairness and predictive accuracy. To address this, we introduce a novel comparative framework that leverages Generative Adversarial Network (GAN)-based data augmentation to fix the root cause of data imbalance and enhance user-side fairness without sacrificing recommendation performance. Using Neural Collaborative Filtering (NCF) on the MovieLens dataset, we systematically benchmark two distinct augmentation strategies. First, we evaluate decoupled fairness augmentation, which pairs traditional GAN models (CTGAN, GReaT, and TAEGAN) with external preprocessing mitigations. Second, we evaluate intrinsic fairness-aware augmentation, which employs GAN models with embedded fairness mechanisms (TabFairGAN, DECAF, and CFGAN). Performance is evaluated using a comprehensive suite of fairness and recommendation quality metrics. The results demonstrate that GAN-based augmentation significantly improves user-side fairness while preserving overall recommendation performance. Notably, the most effective approach, TabFairGAN, achieves the best trade-off between fairness and accuracy, recording the lowest Uabs of 0.003 and the highest NDCG@10 of 0.811. These findings establish GAN-based data augmentation as an effective, non-invasive strategy for mitigating user-side bias in recommender systems.