Gender-aware fairness in iterative recommender systems: A simulation study on popularity bias

This study examines how gender-based disparities emerge in recommender systems through feedback loops. While fairness has been studied in static settings, little is known about how repeated user-system interactions impact different demographic groups over time. To address this, we utilize a dynamic simulation framework using synthetic interactions and ten feedback iterations. Based on the MovieLens-1M dataset, users are grouped by gender and evaluated using metrics such as calibration, diversity, and long-tail exposure. Results show that female users consistently receive less favorable outcomes, with popularity bias measures (GAP, MRMC) indicating a growing disadvantage over time. Diversity and novelty scores also decline more sharply for women. These findings suggest that feedback loops can reinforce existing inequalities in recommender systems. The employed framework provides a valuable tool for analyzing the evolution of fairness across iterations and highlights the need for gender-sensitive algorithms that maintain fairness over time.