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

TL;DR

This paper uses system dynamics modeling and simulations to analyze how biases evolve and reinforce in fashion recommender systems, highlighting the dominance of inductive biases and the need for improved debiasing strategies.

Contribution

It introduces a dynamic modeling framework to study bias evolution in fashion recommender systems and evaluates the effectiveness of current debiasing methods.

Findings

Inductive biases have a greater impact than user biases.

Current debiasing strategies are only partially effective.

Biases tend to amplify over time without intervention.

Abstract

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.