Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user/item correspondence. Existing approaches have predominantly depended on auxiliary information, such as user reviews and item tags, to establish inter-domain connectivity, but these resources may become inaccessible due to privacy and commercial constraints.

To address these limitations, our study introduces an in-depth exploration of Target-unknown Cross-domain Recommendation (CDR), which contends with the distinct challenge of lacking target domain information during the training phase in the source domain. We illustrate two critical obstacles inherent to Target-unknown CDR: the lack of an inter-domain bridge due to insufficient user/item correspondence or side information and the potential pitfalls of source-domain training biases when confronting distribution shifts across domains. To surmount these obstacles, we propose the CMCDR framework, a novel approach that leverages causal mechanisms extracted from meta-user/item representations. The CMCDR framework employs a vector-quantized encoder–decoder architecture, enabling the disentanglement of user/item characteristics. We posit that domain-transferable knowledge is more readily discernible from user/item characteristics, i.e., the meta-representations, rather than raw users and items. Capitalizing on these meta-representations, our CMCDR framework adeptly incorporates an attention-driven predictor that approximates the front-door adjustment method grounded in causal theory. This cutting-edge strategy effectively mitigates source-domain training biases and enhances generalization capabilities against distribution shifts. Extensive experiments demonstrate the empirical effectiveness and the rationality of CMCDR for target-unknown cross-domain recommendation.

为了解决推荐系统中普遍存在的数据稀疏问题，我们对非重叠跨域推荐这一新兴领域进行了深入的研究，这种技术有助于跨域交互知识的传输，而无需域间用户/项目对应。现有的方法主要依赖于辅助信息（例如用户评论和项目标签）来建立域间连接，但由于隐私和商业限制，这些资源可能变得无法访问。

为了解决这些限制，我们的研究引入了对目标未知跨域推荐（CDR）的深入探索，它应对源域训练阶段缺乏目标域信息的独特挑战。我们说明了目标未知 CDR 固有的两个关键障碍：由于用户/项目对应或辅助信息不足而缺乏域间桥梁，以及在面对跨域分布变化时源域训练偏差的潜在陷阱。为了克服这些障碍，我们提出了 CMCDR 框架，这是一种利用从元用户/项目表示中提取的因果机制的新颖方法。 CMCDR 框架采用矢量量化编码器-解码器架构，能够解开用户/项目特征。我们假设领域可转移知识更容易从用户/项目特征（即元表示）中辨别出来，而不是原始用户和项目。利用这些元表示，我们的 CMCDR 框架巧妙地结合了注意力驱动的预测器，该预测器近似基于因果理论的前门调整方法。这种前沿策略有效地减轻了源域训练偏差，并增强了针对分布变化的泛化能力。大量的实验证明了CMCDR对于目标未知跨域推荐的实证有效性和合理性。