# Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach

arXiv: https://arxiv.org/abs/2510.21728 · HTML: https://mlsanity.com/paper/2510.21728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [A Review of Modern Fashion Recommender Systems](https://mlsanity.com/paper/2202.02757/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems](https://mlsanity.com/paper/2203.00376/) (2022)
- [A Probabilistic Position Bias Model for Short-Video Recommendation Feeds](https://mlsanity.com/paper/2307.14059/) (2023)
- [End-to-End Image-Based Fashion Recommendation](https://mlsanity.com/paper/2205.02923/) (2022)
- [Reusable Self-Attention Recommender Systems in Fashion Industry Applications](https://mlsanity.com/paper/2301.06777/) (2023)
- [On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis](https://mlsanity.com/paper/2308.12911/) (2023)

## Abstract (arXiv)

Bias in recommender systems not only distorts user experience but also perpetuates and amplifies existing societal stereotypes, particularly in sectors like fashion e-commerce. This study employs a dynamic modeling approach to scrutinize the mechanisms of bias activation and reinforcement within Fashion Recommender Systems (FRS). By leveraging system dynamics modeling and experimental simulations, we dissect the temporal evolution of bias and its multifaceted impacts on system performance. Our analysis reveals that inductive biases exert a more substantial influence on system outcomes than user biases, suggesting critical areas for intervention. We demonstrate that while current debiasing strategies, including data rebalancing and algorithmic regularization, are effective to an extent, they require further enhancement to comprehensively mitigate biases. This research underscores the necessity for advancing these strategies and extending system boundaries to incorporate broader contextual factors such as user demographics and item diversity, aiming to foster inclusivity and fairness in FRS. The findings advocate for a proactive approach in recommender system design to counteract bias propagation and ensure equitable user experiences.