  Recommendation models are usually trained on observational interaction data.
However, observational interaction data could result from users' conformity
towards popular items, which entangles users' real interest. Existing methods
tracks this problem as eliminating popularity bias, e.g., by re-weighting
training samples or leveraging a small fraction of unbiased data. However, the
variety of user conformity is ignored by these approaches, and different causes
of an interaction are bundled together as unified representations, hence
robustness and interpretability are not guaranteed when underlying causes are
changing. In this paper, we present DICE, a general framework that learns
representations where interest and conformity are structurally disentangled,
and various backbone recommendation models could be smoothly integrated. We
assign users and items with separate embeddings for interest and conformity,
and make each embedding capture only one cause by training with cause-specific
data which is obtained according to the colliding effect of causal inference.
Our proposed methodology outperforms state-of-the-art baselines with remarkable
improvements on two real-world datasets on top of various backbone models. We
further demonstrate that the learned embeddings successfully capture the
desired causes, and show that DICE guarantees the robustness and
interpretability of recommendation.
