In recent years, Cross-Domain Recommendation (CDR) has drawn significant
attention, which utilizes user data from multiple domains to enhance the
recommendation performance. However, current CDR methods require sharing user
data across domains, thereby violating the General Data Protection Regulation
(GDPR). Consequently, numerous approaches have been proposed for Federated
Cross-Domain Recommendation (FedCDR). Nevertheless, the data heterogeneity
across different domains inevitably influences the overall performance of
federated learning. In this study, we propose FedHCDR, a novel Federated
Cross-Domain Recommendation framework with Hypergraph signal decoupling.
Specifically, to address the data heterogeneity across domains, we introduce an
approach called hypergraph signal decoupling (HSD) to decouple the user
features into domain-exclusive and domain-shared features. The approach employs
high-pass and low-pass hypergraph filters to decouple domain-exclusive and
domain-shared user representations, which are trained by the local-global
bi-directional transfer algorithm. In addition, a hypergraph contrastive
learning (HCL) module is devised to enhance the learning of domain-shared user
relationship information by perturbing the user hypergraph. Extensive
experiments conducted on three real-world scenarios demonstrate that FedHCDR
outperforms existing baselines significantly.

这项研究提出了一种名为 FedHCDR 的新型分布式跨领域推荐框架，通过超图信号解耦来解决不同领域数据异构性的问题，并且通过超图对于领域共享用户关系信息的学习来提升推荐性能。实验结果表明，FedHCDR 在三个真实场景中明显优于现有基准模型。