Online recommender systems have been shown to be vulnerable to group shilling attacks in which attackers of a shilling group collaboratively inject fake profiles with the aim of increasing or decreasing the frequency that particular items are recommended. Existing detection methods mainly use the frequent itemset (dense subgraph) mining or clustering method to generate candidate groups and then utilize the hand-crafted features to identify shilling groups. However, such two-stage detection methods have two limitations. On the one hand, due to the sensitivity of support threshold or clustering parameters setting, it is difficult to guarantee the quality of candidate groups generated. On the other hand, they all rely on manual feature engineering to extract detection features, which is costly and time-consuming. To address these two limitations, we present a shilling group detection method based on graph convolutional network. First, we model the given dataset as a graph by treating users as nodes and co-rating relations between users as edges. By assigning edge weights and filtering normal user relations, we obtain the suspicious user relation graph. Second, we use principal component analysis to refine the rating features of users and obtain the user feature matrix. Third, we design a three-layer graph convolutional network model with a neighbor filtering mechanism and perform user classification by combining both structure and rating features of users. Finally, we detect shilling groups through identifying target items rated by the attackers according to the user classification results. Extensive experiments show that the classification accuracy and detection performance (F1-measure) of the proposed method can reach 98.92% and 99.92% on the Netflix dataset and 93.18% and 92.41% on the Amazon dataset.

在线推荐系统已被证明容易受到群体先令攻击，其中先令群体的攻击者协作注入虚假配置文件，目的是增加或减少推荐特定项目的频率。现有的检测方法主要使用频繁项集（密集子图）挖掘或聚类方法生成候选组，然后利用手工特征识别先令组。然而，这种两阶段检测方法有两个局限性。一方面，由于支持阈值或聚类参数设置的敏感性，难以保证生成的候选组的质量。另一方面，它们都依赖人工特征工程来提取检测特征，成本高、耗时长。为了解决这两个限制，我们提出了一种基于图卷积网络的先令组检测方法。首先，我们通过将用户视为节点并将用户之间的共同关系视为边，将给定的数据集建模为图。通过分配边权重和过滤正常用户关系，我们得到可疑用户关系图。其次，我们使用主成分分析来细化用户的评分特征并获得用户特征矩阵。第三，我们设计了一个具有邻居过滤机制的三层图卷积网络模型，并结合用户的结构和评分特征进行用户分类。最后，我们通过根据用户分类结果识别攻击者评分的目标项目来检测先令组。