Radhofan Azizi Ramdhani

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

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...