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.

翻译：推荐系统旨在从观测反馈中学习用户偏好，涵盖评分预测、点击后转化率预测等基础任务。然而，观测反馈通常存在选择偏差和数据稀疏性问题，有偏且不充分的反馈会严重降低推荐系统在准确性和排序方面的性能。现有解决方案（如数据插补和逆倾向评分）高度依赖于额外训练的插补或倾向模型。本文提出一种面向推荐的新型反事实对比学习框架CounterCLR，通过利用对比学习进展处理非随机缺失数据问题。具体而言，该框架采用名为CauNet的深度表示网络推断推荐中的非随机缺失数据，并通过引入自监督对比学习任务进行用户偏好建模。我们的CounterCLR无需额外模型或估计器即可缓解选择偏差问题，同时在稀疏数据情况下增强泛化能力。真实数据集上的实验证明了该方法的有效性和优越性。