论文标题
基于GNN的协作过滤中的重新访问项目促销：掩盖的目标拓扑攻击视角
Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
论文作者
论文摘要
基于图形神经网络（GNN）的协作过滤（CF）引起了电子商务和社交媒体平台的越来越多的关注。但是，仍然缺乏评估此类CF系统在部署中的鲁棒性的努力。这项工作从根本上与现有攻击不同，重新审视了项目促销任务，并首次从目标拓扑攻击的角度重新制定了项目。具体而言，我们首先开发了目标攻击公式，以最大程度地提高目标项目的受欢迎程度。然后，我们利用基于梯度的优化来找到解决方案。但是，由于图的离散性质，我们观察到梯度估计经常显得嘈杂，从而导致攻击能力降解。为了解决嘈杂的梯度效应，我们提出了一个掩盖的攻击目标，可以显着增强拓扑攻击能力。此外，我们为提出的攻击设计了一种计算高效的方法，因此可以评估大型CF系统。两个现实世界数据集的实验显示了我们攻击在更实际分析基于GNN的CF鲁棒性方面的有效性。
Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to evaluate the robustness of such CF systems in deployment. Fundamentally different from existing attacks, this work revisits the item promotion task and reformulates it from a targeted topological attack perspective for the first time. Specifically, we first develop a targeted attack formulation to maximally increase a target item's popularity. We then leverage gradient-based optimizations to find a solution. However, we observe the gradient estimates often appear noisy due to the discrete nature of a graph, which leads to a degradation of attack ability. To resolve noisy gradient effects, we then propose a masked attack objective that can remarkably enhance the topological attack ability. Furthermore, we design a computationally efficient approach to the proposed attack, thus making it feasible to evaluate large-large CF systems. Experiments on two real-world datasets show the effectiveness of our attack in analyzing the robustness of GNN-based CF more practically.
