Abstract: Although the click farming on e-commerce platform improves the store profits to some extent, but it raises the promotion cost of e-commerce platform, which leads to a serious problem of reputation security, and on the other hand, it misleads consumers with property loss. To solve these problems, an intelligent method named SVM-NB was proposed for detecting the click farming on e-commerce platform for users, and a method of constructing characteristics of click farming was also put forward. Firstly, the relevant data of commodity were collected to create an eigenvalue database. Then a classifier was established based on Support Vector Machine (SVM) algorithm with supervised learning, so as to judge the result of click farming. Finally, the click farming probability of goods was calculated by using Naive Bayes (NB), which can provides users with a reference for their shopping. The reasonality and accuracy of the proposed SVM-NB method was validated by K-fold cross validation algorithm, and the accuracy reached 95.0536%.

Key words: information content identification, phenomenon of click farming, Support Vector Machine (SVM), K-fold cross validation

摘要： 电商平台的刷单行为在一定程度上提高了店铺收益，但是刷单行为一方面抬高了电商平台的推广成本，导致了严重的信誉安全问题；另一方面，虚假的刷单信息致使消费者易受误导，从而造成财产损失。针对电商平台刷单现象，提出面向用户的电商平台刷单行为智能检测方法——SVM-NB算法，并提出构建刷单特征值方法。首先收集商品的相关数据，建立特征值数据库；其次利用基于有监督学习的支持向量机（SVM）算法建立分类器，求解刷单行为的判断结果；最后通过朴素贝叶斯公式计算商品刷单行为的概率，反馈给买家，为其提供购物的参考数据。通过K折交叉验证算法验证了SVM-NB算法应用的合理性和准确性，实验条件下计算结果的准确率高达95.0536%。

关键词: 信息内容识别, 刷单现象, 支持向量机, K折交叉验证