Summary of Fedhcdr: Federated Cross-domain Recommendation with Hypergraph Signal Decoupling, by Hongyu Zhang et al.
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
by Hongyu Zhang, Dongyi Zheng, Lin Zhong, Xu Yang, Jiyuan Feng, Yunqing Feng, Qing Liao
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Federated Cross-Domain Recommendation framework, called FedHCDR, is proposed to address the data heterogeneity challenge in federated learning. The framework utilizes hypergraph signal decoupling (HSD) to decouple user features into domain-exclusive and domain-shared features. This approach employs high-pass and low-pass hypergraph filters to train local-global bi-directional transfer algorithms and enhance learning of domain-shared user relationship information through a hypergraph contrastive learning (HCL) module. Experimental results on three real-world scenarios demonstrate the significant outperformance of FedHCDR over existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedHCDR is a new way to recommend things people might like, based on data from multiple sources. Right now, these methods require sharing personal information across all domains, which isn’t allowed under certain laws. To solve this problem, researchers have proposed “federated” approaches that keep user data private. However, these approaches often struggle with differences in the way data is stored and processed across different domains. This new framework, FedHCDR, tries to fix this issue by using a special kind of graph-based processing to separate out information that’s unique to each domain from information that’s shared across all domains. The results show that FedHCDR performs much better than other methods. |
Keywords
* Artificial intelligence * Federated learning
