Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the over-representation of high rating values. Although each bias has been studied independently, their combined effect, to which we refer to as multifactorial bias, remains underexplored. In this work, we examine how multifactorial bias influences item-side fairness, focusing on exposure bias, which reflects the unequal visibility of items in recommendation outputs. Through simulation studies, we find that positivity bias is disproportionately concentrated on popular items, further amplifying their over-exposure. Motivated by this insight, we adapt a percentile-based rating transformation as a pre-processing strategy to mitigate multifactorial bias. Experiments using six recommendation algorithms across four public datasets show that this approach improves exposure fairness with negligible accuracy loss. We also demonstrate that integrating this pre-processing step into post-processing fairness pipelines enhances their effectiveness and efficiency, enabling comparable or better fairness with reduced computational cost. These findings highlight the importance of addressing multifactorial bias and demonstrate the practical value of simple, data-driven pre-processing methods for improving fairness in recommender systems.

翻译：流行度偏差与积极性偏差是推荐系统中两种显著的偏差来源。二者均源于输入数据，通过推荐模型传播，并导致不公平或次优的结果。流行度偏差发生在少数物品获得绝大多数交互时，而积极性偏差则源于高评分值的过度表征。尽管每种偏差已被独立研究，但二者的综合效应——我们称之为多因子偏差——仍未得到充分探索。本文研究了多因子偏差如何影响物品侧公平性，重点关注反映推荐输出中物品可见性不平等问题的曝光偏差。通过模拟研究，我们发现积极性偏差不成比例地集中在流行物品上，进一步放大了它们的过度曝光。基于这一发现，我们采用了一种基于百分位数的评分转换方法作为预处理策略，以缓解多因子偏差。在四个公共数据集上使用六种推荐算法进行的实验表明，该方法能以可忽略的精度损失提升曝光公平性。我们还证明，将此预处理步骤整合到后处理公平性流程中，能提升其效果与效率，在降低计算成本的同时实现相当或更好的公平性。这些发现凸显了解决多因子偏差的重要性，并展示了简单、数据驱动的预处理方法对于提升推荐系统公平性的实用价值。