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.

翻译：近年来，跨域推荐（CDR）因其利用多领域用户数据增强推荐性能而受到广泛关注。然而，现有CDR方法需要跨域共享用户数据，这违反了《通用数据保护条例》（GDPR）。为此，众多研究者提出了联邦跨域推荐（FedCDR）方法。但不同领域间的数据异质性不可避免地影响联邦学习的整体性能。本研究提出FedHCDR——一种基于超图信号解耦的新型联邦跨域推荐框架。具体而言，为应对跨域数据异质性，我们引入超图信号解耦（HSD）方法，将用户特征解耦为领域专属特征与领域共享特征。该方法采用高通与低通超图滤波器分别解耦领域专属与领域共享的用户表示，并通过局部-全局双向迁移算法进行训练。此外，我们设计了超图对比学习（HCL）模块，通过对用户超图施加扰动来增强领域共享用户关系信息的学习。在三个真实场景上的大量实验表明，FedHCDR显著优于现有基线方法。