摘要
【目的】以用户-评论-商户虚假度增强关系为基础,提出一种基于用户偏差的虚假度迭代修正模型(URS-FDIRM),以有效识别虚假用户。【方法】分别采用均值法、JS散度和KL散度三种方法测度用户内容偏差和用户行为偏差,基于马蜂窝平台的实验数据构建URS-FDIRM模型识别虚假用户。【结果】三种方法均能有效测度用户的内容偏差和行为偏差,其中,采用均值法的URS-FDIRM模型对虚假用户识别效果最佳,F1值达92.57%。【局限】该方法主要结合常规偏差度量方法提取用户偏差指标,未探索包括更多用户行为特征的偏差度量方法,一定程度影响了虚假用户识别效果。【结论】考虑用户-评论之间的内容偏差和商户-用户之间的行为偏差,能捕获更多的用户虚假度线索,揭示用户-评论-商户三者虚假度的相互关系,为异常用户行为监测提供参考。
[Objective] Based on the user-review-shop(URS) and the fake degree relationship, this paper proposes a model based on user deviation, aiming to effectively identify fake accounts. [Methods] First, we measured the user’s deviations of contents and behaviors with the means method, JS divergence and KL divergence respectively. Then, we constructed the URS-FDIRM model to identify fake users with experimental data from mafengwo. com. [Results] The proposed models effectively measured the user’s deviations of contents and behaviors. The F1 value of URS-FDIRM model reached 92.57%. [Limitations] This method mainly uses the conventional measurements to extract the deviation index and did not include more deviation measurements with user behaviors. [Conclusions] The proposed method could help us reveal the false relationship among users,reviews and shops, and monitor abnormal user behaviors.
作者
孟园
王悦
Meng Yuan;Wang Yue(School of Statistics and Information,Shanghai University of International Business and Economics,Shanghai 201620,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第6期55-70,共16页
Data Analysis and Knowledge Discovery
基金
上海市哲学社会科学规划课题一般项目(项目编号:2020BGL009)
上海对外经贸大学研究生科研创新培育项目(项目编号:2021-030800-05)的研究成果之一。
关键词
用户偏差
增强关系
虚假用户识别
均值偏差
虚假度
User Deviation
Reinforcing Relationship
Fake User Identification
Mean Deviation
Fake Degree
