  In recommendation systems, the existence of the missing-not-at-random (MNAR)
problem results in the selection bias issue, degrading the recommendation
performance ultimately. A common practice to address MNAR is to treat missing
entries from the so-called "exposure" perspective, i.e., modeling how an item
is exposed (provided) to a user. Most of the existing approaches use heuristic
models or re-weighting strategy on observed ratings to mimic the
missing-at-random setting. However, little research has been done to reveal how
the ratings are missing from a causal perspective. To bridge the gap, we
propose an unbiased and robust method called DENC (De-bias Network Confounding
in Recommendation) inspired by confounder analysis in causal inference. In
general, DENC provides a causal analysis on MNAR from both the inherent factors
(e.g., latent user or item factors) and auxiliary network's perspective.
Particularly, the proposed exposure model in DENC can control the social
network confounder meanwhile preserves the observed exposure information. We
also develop a deconfounding model through the balanced representation learning
to retain the primary user and item features, which enables DENC generalize
well on the rating prediction. Extensive experiments on three datasets validate
that our proposed model outperforms the state-of-the-art baselines.
