融合时间序列与多尺度特征的虚假评论识别方法

摘要

结合时间序列与多尺度特征, 提出一种改进的虚假评论识别方法.考虑时间因素对评分及其分布的影响, 构建基于多维时间序列的虚假评论识别模型提取异常评论特征, 并对异常评论特征进行层次划分, 根据多尺度特征思想获取基准尺度特征及细分尺度特征.采用基于密度峰值的聚类算法识别虚假评论, 并提高虚假评论识别模型的抗噪能力.实验结果表明, 与基于基准尺度特征和多尺度特征的密度峰值聚类虚假评论识别方法相比, 该方法的AUC值达到92％, 虚假评论识别正确率更高.%This paper proposes an improved fake reviews identification method combining time series with multi-scale features.Considering the influence of time factors on the ratings and its distribution, it constructs fake reviews identification model based on multi-dimensional time series to extract abnormal features.It divides abnormal review features into groups, benchmark features and subdivision scale features are extracted according to multi-scale feature idea.To improve the noise immunity of false reviews identification models, it uses a clustering algorithm based on density peaks to identify fake views.Experimental results show that this method has higher identification correct rate of fake reviews and AUC value reach 92％ compared with false comment identification method through density peaks clustering based on benchmark scale feature and multi-scale feature.