FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
This work addresses privacy concerns in cross-domain recommendation by enabling federated learning without sharing user data, though it is incremental as it builds on prior federated CDR methods.
The paper tackles the problem of data heterogeneity in federated cross-domain recommendation by proposing FedHCDR, which uses hypergraph signal decoupling to separate user features into domain-exclusive and domain-shared components, resulting in significant performance improvements over existing baselines in experiments on three real-world scenarios.
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.