FedGR: Cross-platform federated group recommendation system with hypergraph neural networks

Abstract

Group recommendation systems are widely applied in social media, e-commerce, and diverse platforms. These systems face challenges associated with data privacy constraints and protection regulations, impeding the sharing of user data for model improvement. To address the issue of data silos, federated learning emerges as a viable solution. However, difficulties arise due to the non-independent and non-identically distributed nature of data across different platforms, affecting performance. Furthermore, conventional federated learning often overlooks individual differences among stakeholders. In response to these challenges, we propose a pioneering cross-platform federated group recommendation system named FedGR. FedGR integrates hypergraph convolution, attention aggregation, and fully connected fusion components with federated learning to ensure exceptional model performance while preserving the confidentiality of private data. Additionally, we introduce a novel federated model aggregation strategy that prioritizes models with high training effectiveness, thereby improving overall model performance. To address individual differences, we design a temporal personalization update strategy for updating item representations, allowing local models to focus more on their individual characteristics. To evaluate FedGR, we apply our approach to three real-world datasets, demonstrating the robust capabilities of our cross-platform group recommendation system.