Neural graph collaborative filtering for privacy preservation based on federated transfer learning

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.

A cross-platform recommendation system from Facebook to Instagram

“…There has been a significant amount of research in the field of recommendation systems, with a focus on improving the accuracy and personalization of recommendations (Kouki et al, 2019;Liu et al, 2022;Pei et al, 2019). This has included the development of new algorithms and techniques, such as matrix factorization (Bin and Sun, 2021;Koren et al, 2009) and deep learning (Zeng et al, 2021;Zhang et al, 2019Zhang et al, , 2021, as well as the incorporation of additional information, such as user context and social connections (Chamoso et al, 2018).…”