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

Year 2025, Volume: 14 Issue: 4, 1199 - 1210, 15.10.2025

Yıldız Zoralioğlu , Emre Yalçın

Abstract

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.

Keywords

Gender fairness , recommender systems , feedback loop , popularity bias , demographic disparity

Ethical Statement

The authors declare that they have no conflict of interest.

Thanks

This work is based on the Master’s thesis of Yildiz Zoralioglu and builds upon the research conducted as part of her graduate studies.

İteratif öneri sistemlerinde cinsiyete-duyarlı adalet: Popülerlik yanlılığı üzerine bir simülasyon çalışması

Abstract

Bu çalışma, öneri sistemlerinde geri besleme döngüleri yoluyla cinsiyete dayalı eşitsizliklerin nasıl ortaya çıktığını incelemektedir. Adalet konusu durağan ortamlarda araştırılmış olsa da yinelenen kullanıcı-sistem etkileşimlerinin zaman içinde farklı demografik grupları nasıl etkilediği hakkında çok az bilgi bulunmaktadır. Bu durumu ele almak için, sentetik etkileşimler ve on geri besleme iterasyonu içeren dinamik bir simülasyon çerçevesi kullanılmıştır. MovieLens-1M veri kümesine dayalı olarak kullanıcılar cinsiyete göre gruplanmış ve kalibrasyon, çeşitlilik ve uzun kuyruk içeriklere erişim gibi metriklerle değerlendirilmiştir. Sonuçlar, kadın kullanıcıların sistemden sürekli olarak daha olumsuz sonuçlar aldığını göstermekte; GAP ve MRMC gibi popülerlik yanlılığı metrikleri ise zamanla artan bir dezavantajı ortaya koymaktadır. Ayrıca, kadın kullanıcılar için çeşitlilik ve yenilik skorlarının daha keskin bir şekilde düştüğü gözlemlenmiştir. Bu bulgular, geri besleme döngülerinin öneri sistemlerinde mevcut eşitsizlikleri pekiştirebileceğini ortaya koymakta ve zaman içinde adaleti koruyacak cinsiyete duyarlı algoritmalara duyulan ihtiyacı vurgulamaktadır.

Keywords

Cinsiyet adaleti , öneri sistemleri , geri besleme döngüsü , popülerlik yanlılığı , demografik eşitsizlik

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