Neural graph collaborative filtering for privacy preservation based on federated transfer learning
ISSN: 0264-0473
Article publication date: 3 November 2022
Issue publication date: 29 November 2022
Abstract
Purpose
In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation effectiveness. However, due to privacy risk concerns, it is essential to balance the accuracy of personalized recommendations with privacy protection. Accordingly, this paper aims to propose a neural graph collaborative filtering personalized recommendation framework based on federated transfer learning (FTL-NGCF), which achieves high-quality personalized recommendations with privacy protection.
Design/methodology/approach
FTL-NGCF uses a third-party server to coordinate local users to train the graph neural networks (GNN) model. Each user client integrates user–item interactions into the embedding and uploads the model parameters to a server. To prevent attacks during communication and thus promote privacy preservation, the authors introduce homomorphic encryption to ensure secure model aggregation between clients and the server.
Findings
Experiments on three real data sets (Gowalla, Yelp2018, Amazon-Book) show that FTL-NGCF improves the recommendation performance in terms of recall and NDCG, based on the increased consideration of privacy protection relative to original federated learning methods.
Originality/value
To the best of the authors’ knowledge, no previous research has considered federated transfer learning framework for GNN-based recommendation. It can be extended to other recommended applications while maintaining privacy protection.
Keywords
Acknowledgements
Funding: Supported by “the Fundamental Research Funds for the Central Universities (Grant no: 31512211301/113)”.
Citation
Liu, Y., Fang, S., Wang, L., Huan, C. and Wang, R. (2022), "Neural graph collaborative filtering for privacy preservation based on federated transfer learning", The Electronic Library, Vol. 40 No. 6, pp. 729-742. https://doi.org/10.1108/EL-06-2022-0141
Publisher
:Emerald Publishing Limited
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