Trust-GRS: A Trustworthy Training Framework for Graph Neural Network Based Recommender Systems Against Shilling Attacks

Abstract

Graph neural network (GNN) based recommender systems have been widely used in diverse service platforms as they can more effectively capture users' interests. Nevertheless, recent investigations have revealed that the neighborhood aggregation and contrastive learning mechanisms render GNN-based recommender systems more vulnerable to fake profile injection attacks, i.e., shilling attacks. Despite numerous defenses against shilling attacks having emerged, these approaches still face certain challenges, such as the demand for prior knowledge and the difficulty in defending against multiple attacks. Therefore, this paper proposes a two-stage trustworthy GNN-based recommender systems training framework (Trust-GRS), which models the probability of data being fake in a zero-knowledge scenario and establishes a trustworthy neighborhood aggregation and contrastive learning. Through extensive experiments on multiple benchmark datasets against 12 state-of-the-art shilling attacks, we demonstrate that Trust-GRS substantially mitigates the influence of fake data in all attacks, up to 100%, while preserving the original recommendation performance. Benefiting from the absence of the requirement for prior knowledge, Trust-GRS holds significant application value for real-world recommendation platforms.