论文标题
电子商务中的大规模骗子竞标者检测
Large-Scale Shill Bidder Detection in E-commerce
论文作者
论文摘要
用户反馈是建立和维持对电子商务平台信任的最有效方法之一。不幸的是，不诚实的卖家经常向后弯腰，以操纵用户的反馈或放置假竞标，以增加自己的销售和损害竞争对手。用户反馈的黑色市场得到了众多Shill Bidders的支持，在合法的电子商务之上繁荣起来。在本文中，我们通过分析数亿用户进行数十亿笔交易的用户，根据大规模数据研究Shill竞标者的生态系统，我们建议一种基于机器学习的方法来识别有条不紊地提供不诚实反馈的用户社区。我们的结果表明，（1）可以根据交易和反馈统计数据来确定Shill Bidders的精确度； （2）与合法的买家和卖方相反，Shill Bidders形成了互相支持的集团。
User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users' feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.
