Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach
This addresses bias propagation and fairness issues in fashion e-commerce recommender systems, but it is incremental as it builds on existing debiasing strategies.
This study tackled the problem of bias evolution in fashion recommender systems by modeling its mechanisms and impacts, finding that inductive biases have a more substantial influence on system outcomes than user biases, with current debiasing strategies requiring enhancement for comprehensive mitigation.
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.