Selection bias in recommender system arises from the recommendation process of system filtering and the interactive process of user selection. Many previous studies have focused on addressing selection bias to achieve unbiased learning of the prediction model, but ignore the fact that potential outcomes for a given user-item pair may vary with the treatments assigned to other user-item pairs, named neighborhood effect. To fill the gap, this paper formally formulates the neighborhood effect as an interference problem from the perspective of causal inference and introduces a treatment representation to capture the neighborhood effect. On this basis, we propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect. We further develop two new estimators for estimating the proposed ideal loss. We theoretically establish the connection between the proposed and previous debiasing methods ignoring the neighborhood effect, showing that the proposed methods can achieve unbiased learning when both selection bias and neighborhood effect are present, while the existing methods are biased. Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods.

翻译：推荐系统中的选择偏差源于系统过滤的推荐过程与用户选择的交互过程。以往许多研究专注于解决选择偏差以实现预测模型的无偏学习，但忽略了特定用户-项目对的潜在结果可能因其他用户-项目对的干预分配而异，这一现象被称为邻域效应。为弥补这一不足，本文从因果推断视角将邻域效应形式化为干扰问题，并引入一种处理表征以捕捉邻域效应。在此基础上，我们提出了一种新颖的理想损失函数，可用于处理存在邻域效应时的选择偏差。我们进一步开发了两种新估计量来估计所提出的理想损失。我们从理论上建立了本文方法与忽略邻域效应的现有去偏方法之间的联系，表明本文方法在同时存在选择偏差与邻域效应时能实现无偏学习，而现有方法则存在偏差。通过大量半合成实验和真实世界实验，验证了所提出方法的有效性。