FedHCDR：基于超图信号解耦的联邦跨域推荐
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
AI总结：
针对联邦跨域推荐中的数据异质性问题，提出FedHCDR框架，通过超图信号解耦分离领域专属与共享特征，并结合超图对比学习提升共享关系建模，在多个真实场景中显著优于现有方法。
摘要：
近年来，跨域推荐（Cross-Domain Recommendation, CDR）引起了广泛关注，它利用来自多个领域的用户数据来提升推荐性能。然而，现有的CDR方法需要在域之间共享用户数据，从而违反了《通用数据保护条例》（GDPR）。因此，针对联邦跨域推荐（Federated Cross-Domain Recommendation, FedCDR）已提出了大量方法。尽管如此，不同领域之间的数据异质性不可避免地影响联邦学习的整体性能。在本研究中，我们提出FedHCDR，一种新颖的基于超图信号解耦的联邦跨域推荐框架。具体而言，为了解决跨域数据异质性问题，我们引入一种称为超图信号解耦（hypergraph signal decoupling, HSD）的方法，将用户特征解耦为领域专属特征和领域共享特征。该方法采用高通和低通超图滤波器来解耦领域专属和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，设计了一个超图对比学习（hypergraph contrastive learning, HCL）模块，通过扰动用户超图来增强领域共享用户关系信息的学习。在三个真实世界场景上进行的大量实验表明，FedHCDR显著优于现有基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
