In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to capture comprehensive user preferences in order to ultimately enhance the recommendation accuracy in both data-richer and data-sparser domains simultaneously. However, in addition to users' true preferences, the user-item interactions might also be affected by confounders (e.g., free shipping, sales promotion). As a result, dual-target CDR has to meet two challenges: (1) how to effectively decouple observed confounders, including single-domain confounders and cross-domain confounders, and (2) how to preserve the positive effects of observed confounders on predicted interactions, while eliminating their negative effects on capturing comprehensive user preferences. To address the above two challenges, we propose a Causal Deconfounding framework via Confounder Disentanglement for dual-target Cross-Domain Recommendation, called CD2CDR. In CD2CDR, we first propose a confounder disentanglement module to effectively decouple observed single-domain and cross-domain confounders. We then propose a causal deconfounding module to preserve the positive effects of such observed confounders and eliminate their negative effects via backdoor adjustment, thereby enhancing the recommendation accuracy in each domain. Extensive experiments conducted on five real-world datasets demonstrate that CD2CDR significantly outperforms the state-of-the-art methods.

翻译：近年来，双目标跨域推荐被提出以捕获全面的用户偏好，旨在同时提升数据较丰富域和数据较稀疏域的推荐精度。然而，除了用户的真实偏好外，用户-物品交互还可能受到混杂因子（例如免运费、促销活动）的影响。因此，双目标跨域推荐需应对两大挑战：(1) 如何有效解耦观测到的混杂因子，包括单域混杂因子和跨域混杂因子；(2) 如何在保留观测混杂因子对预测交互的积极影响的同时，消除其对捕获全面用户偏好的负面影响。为解决上述挑战，我们提出一种基于混杂因子解耦的双目标跨域推荐因果去混杂框架，称为CD2CDR。在CD2CDR中，我们首先提出一个混杂因子解耦模块，以有效解耦观测到的单域与跨域混杂因子。随后，我们提出一个因果去混杂模块，通过后门调整保留此类观测混杂因子的积极影响并消除其负面影响，从而提升各域的推荐精度。在五个真实数据集上的大量实验表明，CD2CDR显著优于当前最先进的方法。