@inproceedings{
yang2024federated,
title={Federated Graph Learning for Cross-Domain Recommendation},
author={Ziqi Yang and Zhaopeng Peng and Zihui Wang and Jianzhong Qi and Chaochao Chen and Weike Pan and Chenglu Wen and Cheng Wang and Xiaoliang Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=UBpPOqrBKE}
}
Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods.
Federated Graph Learning for Cross-Domain Recommendation
Ziqi Yang Zhaopeng Peng Zihui Wang Jianzhong Qi Chaochao Chen Weike Pan Chenglu Wen Cheng Wang Xiaoliang Fan