用系统动力学分析时尚推荐中的偏见演化，发现模型自身偏见影响更大。
Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach
- 构建系统动力学模型模拟偏见随时间演变过程。
- 发现模型诱导偏见比用户偏见对结果影响更显著。
- 适合关注公平性与推荐系统设计的研究者阅读。
推荐系统中的偏见不仅扭曲用户体验，还加剧和放大社会刻板印象，尤其在时尚电商领域。本研究采用动态建模方法，剖析时尚推荐系统（FRS）中偏见激活与强化机制。通过系统动力学建模与实验仿真，我们分析了偏见的时序演化及其对系统性能的多方面影响。结果显示，模型的归纳偏见对系统结果的影响大于用户偏见，提示干预重点应放在模型层面。尽管现有去偏策略（如数据重平衡、算法正则化）部分有效，但仍需改进以全面缓解偏见。研究强调需拓展系统边界，纳入用户人口统计特征与商品多样性等上下文因素，以促进FRS的包容性与公平性。结论呼吁在推荐系统设计中采取主动策略，遏制偏见传播，保障公平体验。
原文摘要 · Abstract (English)
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.
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