论文标题
基于用户的网络嵌入用于集体意见垃圾邮件发送者检测
User-based Network Embedding for Collective Opinion Spammer Detection
论文作者
论文摘要
由于在线评论背后具有巨大的商业兴趣，因此，垃圾邮件发送者的垃圾邮件垃圾邮件备受关注。为了进一步增强垃圾邮件评论的影响，垃圾邮件发送者通常在短时间内进行垃圾邮件审稿人进行协作，其活动被称为集体意见垃圾邮件运动。随着垃圾邮件活动活动的目标和成员的经常变化，一些垃圾邮件发送者还模仿正常的购买以掩盖身份，这使垃圾邮件发送者的检测具有挑战性。在本文中，我们提出了一种基于无监督的网络嵌入方法，以共同利用不同类型的关系，例如，直接的共同行为关系和间接共同审查的关系有效地表示用户检测集体意见垃圾邮件发送者的相关性。在数据集AmazonCN和Yelphotel上，我们的方法的平均改进分别为[14.09％，12.04％]和[16.25％，12.78％]，分别在AP和AUC方面。
Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the influence of spam reviews, spammers often collaboratively post spam reviewers within a short period of time, the activities of whom are called collective opinion spam campaign. As the goals and members of the spam campaign activities change frequently, and some spammers also imitate normal purchases to conceal identity, which makes the spammer detection challenging. In this paper, we propose an unsupervised network embedding-based approach to jointly exploiting different types of relations, e.g., direct common behaviour relation and indirect co-reviewed relation to effectively represent the relevances of users for detecting the collective opinion spammers. The average improvements of our method over the state-of-the-art solutions on dataset AmazonCn and YelpHotel are [14.09%,12.04%] and [16.25%,12.78%] in terms of AP and AUC, respectively.
