标题： 时尚推荐系统中偏差演化的建模：系统动力学方法 显示英文标题

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

摘要： 推荐系统中的偏差不仅会扭曲用户体验，还会延续并放大现有的社会刻板印象，尤其是在时尚电子商务等领域。 本研究采用动态建模方法，仔细分析偏差激活和强化在时尚推荐系统（FRS）中的机制。 通过利用系统动力学建模和实验模拟，我们剖析了偏差的时间演化及其对系统性能的多方面影响。 我们的分析表明，归纳偏差对系统结果的影响比用户偏差更大，这表明了干预的关键领域。 我们证明了尽管当前的去偏策略，包括数据再平衡和算法正则化，在一定程度上是有效的，但它们需要进一步改进，以全面减轻偏差。 这项研究强调了改进这些策略并扩展系统边界以纳入更广泛的上下文因素（如用户人口统计信息和物品多样性）的必要性，旨在促进FRS中的包容性和公平性。 研究结果倡导在推荐系统设计中采取主动措施，以对抗偏差传播并确保公平的用户体验。 显示英文摘要

摘要： 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.