Cross-domain recommendation forms a crucial component in recommendation systems. It leverages auxiliary information through source domain tasks or features to enhance target domain recommendations. However, incorporating inconsistent source domain tasks may result in insufficient cross-domain modeling or negative transfer. While incorporating source domain features without considering the underlying causal relationships may limit their contribution to final predictions. Thus, a natural idea is to directly train a cross-domain representation on a causality-labeled dataset from the source to target domain. Yet this direction has been rarely explored, as identifying unbiased real causal labels is highly challenging in real-world scenarios. In this work, we attempt to take a first step in this direction by proposing a causality-enhanced framework, named CE-CDR. Specifically, we first reformulate the cross-domain recommendation as a causal graph for principled guidance. We then construct a causality-aware dataset heuristically. Subsequently, we derive a theoretically unbiased Partial Label Causal Loss to generalize beyond the biased causality-aware dataset to unseen cross-domain patterns, yielding an enriched cross-domain representation, which is then fed into the target model to enhance target-domain recommendations. Theoretical and empirical analyses, as well as extensive experiments, demonstrate the rationality and effectiveness of CE-CDR and its general applicability as a model-agnostic plugin. Moreover, it has been deployed in production since April 2025, showing its practical value in real-world applications.

翻译：跨域推荐是推荐系统中的关键组成部分，它通过源域任务或特征利用辅助信息来增强目标域推荐。然而，纳入不一致的源域任务可能导致跨域建模不足或负迁移。而纳入源域特征时若不考虑其潜在的因果关系，可能会限制其对最终预测的贡献。因此，一个自然的思路是直接在从源域到目标域的因果标记数据集上训练跨域表示。然而，由于在现实场景中识别无偏的真实因果标签极具挑战性，这一方向鲜有探索。在本工作中，我们尝试通过提出一个名为CE-CDR的因果增强框架，向此方向迈出第一步。具体而言，我们首先将跨域推荐重新表述为一个因果图，以提供原则性指导。随后，我们启发式地构建一个因果感知数据集。接着，我们推导出一个理论上无偏的部分标签因果损失，以泛化到超出有偏因果感知数据集之外的未见跨域模式，从而得到一个增强的跨域表示，该表示随后被输入目标模型以增强目标域推荐。理论分析、实证分析以及广泛的实验证明了CE-CDR的合理性和有效性，及其作为模型无关插件的普适适用性。此外，该框架自2025年4月起已部署于生产环境，展现了其在现实应用中的实用价值。