  Recommender systems are designed to learn user preferences from observed
feedback and comprise many fundamental tasks, such as rating prediction and
post-click conversion rate (pCVR) prediction. However, the observed feedback
usually suffer from two issues: selection bias and data sparsity, where biased
and insufficient feedback seriously degrade the performance of recommender
systems in terms of accuracy and ranking. Existing solutions for handling the
issues, such as data imputation and inverse propensity score, are highly
susceptible to additional trained imputation or propensity models. In this
work, we propose a novel counterfactual contrastive learning framework for
recommendation, named CounterCLR, to tackle the problem of non-random missing
data by exploiting the advances in contrast learning. Specifically, the
proposed CounterCLR employs a deep representation network, called CauNet, to
infer non-random missing data in recommendations and perform user preference
modeling by further introducing a self-supervised contrastive learning task.
Our CounterCLR mitigates the selection bias problem without the need for
additional models or estimators, while also enhancing the generalization
ability in cases of sparse data. Experiments on real-world datasets demonstrate
the effectiveness and superiority of our method.
