Online product reviews are very important information resources on e-commerce websites and significantly influence consumers’ purchase decisions. Driven by interests, however, some merchants might hire a group of reviewers working together to promote or demote a set of target products by writing fake reviews. Such a collusive fraudulent reviewer group is generally termed a review spammer group and is more harmful to e-commerce websites than individual review spammers. To address this issue, in this paper we propose a label propagation-based approach to detect review spammer groups on e-commerce websites. First, based on the evaluation data of reviewers, we extract the associations between reviewers with respect to review time and product ratings to construct a relationship graph of reviewers. Second, we propose an improved label propagation algorithm with a propagation intensity and an automatic filtering mechanism to find candidate spammer groups based on the constructed reviewer relationship graph. Finally, we propose a ranking algorithm that combines the entropy method and the analytic hierarchy process to rank the candidate spammer groups and thus identify the top-k review spammer groups. The experimental results of the real-world Amazon and Yelp datasets show that the proposed approach performs better than the baseline methods.

在线产品评论是电子商务网站上非常重要的信息资源，并且会显着影响消费者的购买决定。但是，在利益的驱使下，一些商人可能会雇用一组评论者，他们通过撰写虚假评论来共同促进或降级一组目标产品。这种串通欺诈性的审阅者组通常被称为审阅垃圾邮件发件人组，比单个审阅垃圾邮件发件人对电子商务网站的危害更大。为了解决这个问题，本文提出了一种基于标签传播的方法来检测电子商务网站上的垃圾评论发送者群组。首先，基于审稿人的评价数据，提取审稿人之间关于审稿时间和产品评分的关联，以构建审稿人的关系图。第二，我们提出了一种具有传播强度和自动过滤机制的改进标签传播算法，该算法基于构造的审阅者关系图来查找候选垃圾邮件发送者组。最后，我们提出了一种排序算法，将熵方法和层次分析法相结合，对候选垃圾邮件发送者群组进行排名，从而确定排名前k的垃圾邮件发送者群组。真实的Amazon和Yelp数据集的实验结果表明，所提出的方法比基线方法具有更好的性能。我们提出一种结合了熵方法和层次分析法的排序算法，对候选垃圾邮件发送者群组进行排名，从而确定排名前k的垃圾邮件发送者群组。真实的Amazon和Yelp数据集的实验结果表明，所提出的方法比基线方法具有更好的性能。我们提出一种结合了熵方法和层次分析法的排序算法，对候选垃圾邮件发送者群组进行排名，从而确定排名前k的垃圾邮件发送者群组。真实的Amazon和Yelp数据集的实验结果表明，所提出的方法比基线方法具有更好的性能。