  Recommender systems usually rely on observed user interaction data to build
personalized recommendation models, assuming that the observed data reflect
user interest. However, user interacting with an item may also due to
conformity, the need to follow popular items. Most previous studies neglect
user's conformity and entangle interest with it, which may cause the
recommender systems fail to provide satisfying results. Therefore, from the
cause-effect view, disentangling these interaction causes is a crucial issue.
It also contributes to OOD problems, where training and test data are
out-of-distribution. Nevertheless, it is quite challenging as we lack the
signal to differentiate interest and conformity. The data sparsity of pure
cause and the items' long-tail problem hinder disentangled causal embedding. In
this paper, we propose DCCL, a framework that adopts contrastive learning to
disentangle these two causes by sample augmentation for interest and conformity
respectively. Futhermore, DCCL is model-agnostic, which can be easily deployed
in any industrial online system. Extensive experiments are conducted over two
real-world datasets and DCCL outperforms state-of-the-art baselines on top of
various backbone models in various OOD environments. We also demonstrate the
performance improvements by online A/B testing on Kuaishou, a billion-user
scale short-video recommender system.
