Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR works design their elaborate structures relying on overlapping users to propagate the cross-domain information. However, current CDSR methods make closed-world assumptions, assuming fully overlapping users across multiple domains and that the data distribution remains unchanged from the training environment to the test environment. As a result, these methods typically result in lower performance on online real-world platforms due to the data distribution shifts. To address these challenges under open-world assumptions, we design an \textbf{A}daptive \textbf{M}ulti-\textbf{I}nterest \textbf{D}ebiasing framework for cross-domain sequential recommendation (\textbf{AMID}), which consists of a multi-interest information module (\textbf{MIM}) and a doubly robust estimator (\textbf{DRE}). Our framework is adaptive for open-world environments and can improve the model of most off-the-shelf single-domain sequential backbone models for CDSR. Our MIM establishes interest groups that consider both overlapping and non-overlapping users, allowing us to effectively explore user intent and explicit interest. To alleviate biases across multiple domains, we developed the DRE for the CDSR methods. We also provide a theoretical analysis that demonstrates the superiority of our proposed estimator in terms of bias and tail bound, compared to the IPS estimator used in previous work.

翻译：跨领域序列推荐（CDSR）方法旨在解决单领域序列推荐（SDSR）中普遍存在的数据稀疏性和冷启动问题。现有CDSR工作依赖重叠用户来传播跨领域信息，并设计了精巧的结构。然而，当前CDSR方法基于封闭世界假设，即假设多个领域的用户完全重叠，且从训练环境到测试环境的数据分布保持不变。这导致这些方法在面对在线真实平台的数据分布偏移时，通常性能表现较低。为应对开放世界假设下的这些挑战，我们设计了一个面向跨领域序列推荐的**自**适应**多**兴趣**去**偏框架（**AMID**），该框架包含多兴趣信息模块（**MIM**）和双重稳健估计器（**DRE**）。我们的框架可自适应开放世界环境，并能提升大多数现有单领域序列骨干模型在CDSR任务上的性能。通过MIM建立同时考虑重叠与非重叠用户的兴趣组，我们得以有效探索用户意图和显式兴趣。为缓解跨多个领域的偏差，我们专门为CDSR方法开发了DRE。此外，我们提供的理论分析表明，与先前工作中使用的IPS估计器相比，我们提出的估计器在偏差和尾部界方面具有优越性。