Online product reviews are very important information resources on e-commerce websites
and significantly influence consumers' purchase decisions. Driven by interests, however …

Existing shilling attack detection approaches focus mainly on identifying individual attackers
in online recommender systems and rarely address the detection of group shilling attacks in …

Detecting collusive spammers who collaboratively post fake reviews is extremely important
to guarantee the reliability of review information on e-commerce platforms. In this research …

Over the past decade, many approaches have been presented to detect shilling attacks in
collaborative recommender systems. However, these approaches focus mainly on detecting …

The detection of spammer groups has recently gained more attention. However, the existing
spammer group detection approaches rely on manual feature engineering to design spam …

The collusive spamming behavior on e-commerce websites seriously affects the purchase
decisions of consumers and disrupts the fair competition order among merchants. To …

Information security is one of the key issues in e‐commerce Internet of Things (IoT) platform
research. The collusive spamming groups on e‐commerce platforms can write a large …

Existing group shilling attack detection methods mainly depend on human feature
engineering to extract group attack behavior features, which requires a high knowledge cost …

为了能够有效地识别水军, 在以往相关研究基础上, 设置粉丝关注比, 平均发布微博数,
互相关注数, 综合质量评价, 收藏数和阳光信用这6 个特征属性来设计微博水军识别分类器 …

Aiming at group shilling attacks in recommender systems, a shilling group detection
approach based on triangle dense subgraph mining is proposed. First, the user relation …