  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.
