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：跨域序列推荐（CDSR）方法旨在解决单域序列推荐（SDSR）中存在的数据稀疏性和冷启动问题。现有CDSR工作通过依赖重叠用户传播跨域信息来设计其精细化结构。然而，当前CDSR方法基于封闭世界假设，假定多个域中用户完全重叠，且数据分布在训练环境与测试环境之间保持不变。因此，在实际在线平台上，这些方法常因数据分布偏移导致性能较低。为应对开放世界假设下的这些挑战，我们设计了一种跨域序列推荐的**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，并能改进大多数现成单域序列骨干模型以用于CDSR。MIM建立了同时考虑重叠与非重叠用户的兴趣组，从而有效探索用户意图和显式兴趣。为缓解多域间的偏差，我们为CDSR方法开发了DRE。此外，我们提供了理论分析，证明与先前工作中使用的IPS估计器相比，所提估计器在偏差和尾界方面具有优越性。