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.

推荐系统中的选择偏差源于系统过滤和用户选择的交互过程。本研究从因果推断的角度，形式化地将邻域效应作为干预问题，并引入治疗表示来捕捉邻域效应。在此基础上，我们提出了一种新的理想损失函数，用于处理具有邻域效应的选择偏差问题。我们进一步开发了两种新的估计器来估计所提出的理想损失函数。理论上建立了所提出的方法与先前忽略邻域效应的去偏倚方法之间的联系，表明所提出的方法在存在选择偏差和邻域效应时可以实现无偏学习，而现有方法存在偏差。大量的半合成和真实世界实验被进行以证明所提出方法的有效性。