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：跨域序列推荐方法旨在解决单域序列推荐中的数据稀疏性和冷启动问题。现有跨域序列推荐研究依赖重叠用户设计复杂的结构以传播跨域信息。然而，当前的跨域序列推荐方法基于封闭世界假设，假定多个域之间用户完全重叠，且数据分布在训练环境与测试环境中保持不变。因此，由于数据分布偏移，这些方法在在线真实平台上的性能通常较低。为应对开放世界假设下的这些挑战，我们设计了自适应多兴趣去偏框架用于跨域序列推荐，该框架包含多兴趣信息模块和双稳健估计器。我们的框架可适应开放世界环境，并能改进大多数现有单域序列推荐骨干模型的跨域序列推荐能力。多兴趣信息模块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与显性兴趣。为缓解跨域偏差，我们为跨域序列推荐方法开发了双稳健估计器。我们还提供了理论分析，证明与先前工作中使用的逆倾向得分估计器相比，所提估计器在偏差和尾界方面的优越性。