  In recommender system, some feature directly affects whether an interaction
would happen, making the happened interactions not necessarily indicate user
preference. For instance, short videos are objectively easier to be finished
even though the user does not like the video. We term such feature as
confounding feature, and video length is a confounding feature in video
recommendation. If we fit a model on such interaction data, just as done by
most data-driven recommender systems, the model will be biased to recommend
short videos more, and deviate from user actual requirement.
  This work formulates and addresses the problem from the causal perspective.
Assuming there are some factors affecting both the confounding feature and
other item features, e.g., the video creator, we find the confounding feature
opens a backdoor path behind user item matching and introduces spurious
correlation. To remove the effect of backdoor path, we propose a framework
named Deconfounding Causal Recommendation (DCR), which performs intervened
inference with do-calculus. Nevertheless, evaluating do calculus requires to
sum over the prediction on all possible values of confounding feature,
significantly increasing the time cost. To address the efficiency challenge, we
further propose a mixture-of experts (MoE) model architecture, modeling each
value of confounding feature with a separate expert module. Through this way,
we retain the model expressiveness with few additional costs. We demonstrate
DCR on the backbone model of neural factorization machine (NFM), showing that
DCR leads to more accurate prediction of user preference with small inference
time cost.
