Recommender systems use users' historical interactions to learn their preferences and deliver personalized recommendations from a vast array of candidate items. Current recommender systems primarily rely on the assumption that the training and testing datasets have identical distributions, which may not hold true in reality. In fact, the distribution shift between training and testing datasets often occurs as a result of the evolution of user attributes, which degrades the performance of the conventional recommender systems because they fail in Out-of-Distribution (OOD) generalization, particularly in situations of data sparsity. This study delves deeply into the challenge of OOD generalization and proposes a novel model called Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation (CDCOR), which involves employing a domain adversarial network to uncover users' domain-shared preferences and utilizing a causal structure learner to capture causal invariance to deal with the OOD problem. Through extensive experiments on two real-world datasets, we validate the remarkable performance of our model in handling diverse scenarios of data sparsity and out-of-distribution environments. Furthermore, our approach surpasses the benchmark models, showcasing outstanding capabilities in out-of-distribution generalization.

翻译：推荐系统利用用户历史交互来学习其偏好，并从海量候选物品中提供个性化推荐。当前推荐系统主要依赖训练集和测试集具有相同分布的假设，而这一假设在现实中往往不成立。事实上，由于用户属性的演化，训练集与测试集之间的分布偏移时有发生，这导致传统推荐系统性能下降，因为它们无法实现分布外（OOD）泛化，尤其在数据稀疏场景下。本研究深入探讨了分布外泛化的挑战，提出了一种名为跨领域因果偏好学习用于分布外推荐（CDCOR）的新模型。该模型通过引入领域对抗网络挖掘用户的领域共享偏好，并利用因果结构学习器捕捉因果不变性以应对分布外问题。通过在两个真实世界数据集上进行的大量实验，我们验证了该模型在处理数据稀疏和分布外环境多样化场景中的卓越性能。此外，我们的方法超越了基准模型，展现出优异的分布外泛化能力。