This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.

翻译：本文研究跨域序列推荐（CDSR），这是一种利用多个领域（超过三个）的信息生成准确且多样化推荐的前沿方法，并考虑了用户交互的序列特性。此类系统的有效性往往取决于多个领域间复杂的相互作用。在这一动态背景下，负迁移问题应运而生：由于跨领域用户偏好的差异，异质化领域间的知识迁移会导致性能下降。作为解决方案，我们提出了一种新型CDSR框架，通过评估某一领域对另一领域负迁移的程度，并自适应地为相应预测损失分配低权重值来应对该问题。为实现此目标，我们基于合作博弈论测量每个领域对模型性能的边际贡献，从而估算负迁移量。此外，我们开发了一种分层对比学习方法，在实施对比学习时将粗粒度类别序列信息融入细粒度类别（如物品层级）中，以缓解负迁移。尽管细粒度层面领域间的相关性可能较低，但由于类别层级的泛化性和更广泛的偏好特征，该层面可能具有更高相关性。实验结果表明，在两个涉及十个不同领域的真实数据集上，我们的模型在性能上优于现有方法。