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Cross-domain sequential recommendation (CDSR) aims to address the datasparsity problems that exist in traditional sequential recommendation (SR)systems.  The existing approaches aim to design a specific cross-domain unit that cantransfer and propagate information across multiple domains by relying onoverlapping users with abundant behaviors. However, in real-world recommendersystems, CDSR scenarios usually consist of a majority of long-tailed users withsparse behaviors and cold-start users who only exist in one domain. This leadsto a drop in the performance of existing CDSR methods in the real-worldindustry platform. Therefore, improving the consistency and effectiveness ofmodels in open-world CDSR scenarios is crucial for constructing CDSR models(\textit{1st} CH). Recently, some SR approaches have utilized auxiliarybehaviors to complement the information for long-tailed users. However, thesemulti-behavior SR methods cannot deliver promising performance in CDSR, as theyoverlook the semantic gap between target and auxiliary behaviors, as well asuser interest deviation across domains (\textit{2nd} CH).

Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach