Cross-Domain Sequential Recommendation (CDSR) aims to en-hance recommendation quality by transferring knowledge across domains, offering effective solutions to data sparsity and cold-start issues. However, existing methods face three major limitations: (1) they overlook varying contexts in user interaction sequences, resulting in spurious correlations that obscure the true causal relationships driving user preferences; (2) the learning of domain- shared and domain-specific preferences is hindered by gradient conflicts between domains, leading to a seesaw effect where performance in one domain improves at the expense of the other; (3) most methods rely on the unrealistic assumption of substantial user overlap across domains. To address these issues, we propose CoDiS, a context-aware disentanglement framework grounded in a causal view to accurately disentangle domain-shared and domain-specific preferences. Specifically, Our approach includes a variational context adjustment method to reduce confounding effects of contexts, expert isolation and selection strategies to resolve gradient conflict, and a variational adversarial disentangling module for the thorough disentanglement of domain-shared and domain-specific representations. Extensive experiments on three real-world datasets demonstrate that CoDiS consistently outperforms state-of-the-art CDSR baselines with statistical significance. Code is available at:https://anonymous.4open.science/r/CoDiS-6FA0.

翻译：跨域序列推荐旨在通过跨域知识迁移提升推荐质量，为解决数据稀疏性和冷启动问题提供了有效方案。然而，现有方法存在三大局限：（1）忽略用户交互序列中的情境差异，导致虚假关联掩盖了驱动用户偏好的真实因果关系；（2）域间梯度冲突阻碍了共享偏好与特定领域偏好的学习，产生跷跷板效应（一域性能提升以另一域性能下降为代价）；（3）多数方法依赖不切实际的假设——要求各域存在大量用户重叠。针对这些问题，我们提出CoDiS——基于因果视角的情境感知解耦框架，以实现共享偏好与特定领域偏好的精准解耦。具体方法包括：通过变分情境调节方法削弱情境的混杂效应，采用专家隔离与选择策略解决梯度冲突，并构建变分对抗解耦模块实现共享表征与特定领域表征的彻底分离。在三个真实数据集上的大量实验表明，CoDiS在统计显著性上持续优于现有最优的跨域序列推荐基准方法。代码开源地址：https://anonymous.4open.science/r/CoDiS-6FA0