Federated Social Recommendation with Graph Neural Network

Abstract

Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks~(GNNs) also provide recommender systems with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. To this end, we devise a novel framework \textbf{Fe}drated \textbf{So}cial recommendation with \textbf{G}raph neural network (FeSoG). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.

Accepted to ACM TIST

References in corpus (10)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• Graph Convolutional Matrix Completion
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
• Session-based Social Recommendation via Dynamic Graph Attention Networks
• Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
• Robust Federated Recommendation System
• From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems

Cited by in corpus (16)

• A Survey of Graph Neural Networks for Social Recommender Systems
• DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
• Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
• A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
• Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
• FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
• Co-clustering for Federated Recommender System
• Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
• Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
• Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning
• Turning Privacy-preserving Mechanisms against Federated Learning
• HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning
• FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement
• Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks