基于用户的网络嵌入的集体意见垃圾邮件检测
User-based Network Embedding for Collective Opinion Spammer Detection
摘要：
由于在线评论背后的巨大商业利益，大量的垃圾邮件制造者制造垃圾评论以操纵产品声誉。为了进一步提高垃圾评论的影响力，垃圾评论者往往在短时间内协同发布垃圾评论者，这种行为被称为垃圾评论集体运动。由于垃圾邮件活动的目标和成员经常变化，而且一些垃圾邮件发送者还模仿正常购买来隐藏身份，这使得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网络嵌入的方法来联合利用不同类型的关系，例如，直接共同行为关系和间接共同评论关系，以有效地表示用户的相关性，用于检测集体意见垃圾邮件发送者。在数据集AmazonCn和YelpHotel上，我们的方法在AP和AUC方面的平均改进分别为[14.09%，12.04%]和[16.25%，12.78%]。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
