  Recommender systems usually learn user interests from various user behaviors,
including clicks and post-click behaviors (e.g., like and favorite). However,
these behaviors inevitably exhibit popularity bias, leading to some unfairness
issues: 1) for items with similar quality, more popular ones get more exposure;
and 2) even worse the popular items with lower popularity might receive more
exposure. Existing work on mitigating popularity bias blindly eliminates the
bias and usually ignores the effect of item quality. We argue that the
relationships between different user behaviors (e.g., conversion rate) actually
reflect the item quality. Therefore, to handle the unfairness issues, we
propose to mitigate the popularity bias by considering multiple user behaviors.
  In this work, we examine causal relationships behind the interaction
generation procedure in multi-behavior recommendation. Specifically, we find
that: 1) item popularity is a confounder between the exposed items and users'
post-click interactions, leading to the first unfairness; and 2) some hidden
confounders (e.g., the reputation of item producers) affect both item
popularity and quality, resulting in the second unfairness. To alleviate these
confounding issues, we propose a causal framework to estimate the causal
effect, which leverages backdoor adjustment to block the backdoor paths caused
by the confounders. In the inference stage, we remove the negative effect of
popularity and utilize the good effect of quality for recommendation.
Experiments on two real-world datasets validate the effectiveness of our
proposed framework, which enhances fairness without sacrificing recommendation
accuracy.
