Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches aim to design a specific cross-domain unit that can transfer and propagate information across multiple domains by relying on overlapping users with abundant behaviors. However, in real-world recommender systems, CDSR scenarios usually consist of a majority of long-tailed users with sparse behaviors and cold-start users who only exist in one domain. This leads to a drop in the performance of existing CDSR methods in the real-world industry platform. Therefore, improving the consistency and effectiveness of models in open-world CDSR scenarios is crucial for constructing CDSR models (\textit{1st} CH). Recently, some SR approaches have utilized auxiliary behaviors to complement the information for long-tailed users. However, these multi-behavior SR methods cannot deliver promising performance in CDSR, as they overlook the semantic gap between target and auxiliary behaviors, as well as user interest deviation across domains (\textit{2nd} CH).

翻译：跨域序列推荐（CDSR）旨在解决传统序列推荐（SR）系统中存在的数据稀疏问题。现有方法通常通过依赖具有丰富行为的重叠用户，设计特定的跨域单元以实现跨多个域的信息传递与传播。然而，在真实推荐系统中，CDSR场景通常包含大量行为稀疏的长尾用户以及仅存在于单一域的冷启动用户，这导致现有CDSR方法在实际工业平台上的性能下降。因此，提升模型在开放世界CDSR场景中的一致性与有效性，对于构建CDSR模型至关重要（第一项挑战）。近期，部分SR方法利用辅助行为为长尾用户补充信息。然而，这些多行为SR方法在CDSR中无法展现令人满意的性能，因为它们忽略了目标行为与辅助行为之间的语义差异，以及用户兴趣在跨域中的偏移（第二项挑战）。