结合深度置信网络和模糊集的虚假交易识别研究

Combine Deep Belief Networks and Fuzzy Set for Recognition of Fraud Transaction

【目的】解决电子商务平台中存在的虚假交易问题。【方法】依据消费者历史购买和评论行为数据,提出一种结合深度置信网络和模糊集的虚假交易识别方法,通过识别虚假交易的用户(刷客)进行虚假交易的识别。【结果】识别准确率达到89%,与浅层机器学习模型试验结果进行对比,其综合性能有明显提升。【局限】相对于淘宝存在的海量刷客,实验数据较少。仅以淘宝数据作为验证数据,未涉及其他电子商务平台。【结论】本方法能够较好地识别刷客,减少电子商务中的虚假交易问题。

[Objective] To solve the problem of fraud transaction in e-commerce platform. [Methods] This paper proposes a method that combine Deep Belief Networks and fuzzy set based on consumers＇ purchase history and reviews Through recognizing the users in fraud transactions--cheaters to recognize the fraud transactions. [Results] Tested by experiments using the data crawled from Taobao.com, the accuracy can be achieved 89%. Compared with the shallow machine learning model, the comprehensive performance improves significantly. [Limitations] In contrast with the huge normal users and the users in fraud transactions, the experimental data in the paper is relatively small. And the test data only from Taobao.com, lack of the data from the other e-commerce platform to be validated. [Conclusions] The users in fraud transactions can be identified by the method, and the fraud transaction in e-commerce can be reduced.