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清华大学学报(自然科学版)  2015, Vol. 55 Issue (5): 558-564,571    
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基于群体偏好的交易评价可信度
陈元琳, 柴跃廷, 刘义, 徐扬
清华大学 自动化系, 电子商务交易技术国家工程实验室, 北京 100084
Transaction rating credibility based on user group preference
CHEN Yuanlin, CHAI Yueting, LIU Yi, XU Yang
National Engineering Laboratory for E-Commerce Technologies, Department of Automation, Tsinghua University, Beijing 100084, China
全文: PDF(1505 KB)  
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摘要 利用交易评价数据对商品和卖家进行信用评价以供用户参考成为电子商务在线交易平台最基本的服务。然而,目前的信用评价方法很少考虑不同用户间的评价偏好差异,将所有用户的评价同等看待,导致蓄意刷分或恶意差评等信用造假问题屡禁不止。该文提出了一种基于群体偏好的交易评价可信度确立方法。首先采用K-means聚类算法将用户分为3类用户群,通过实证数据分析验证了用户群间明显的评价偏好差异,然后利用评价偏好特征确立每类用户不同类型交易评价的可信度,并提出了动态的交易评价可信度更新策略。该方法能够有效地限制信用造假行为。
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陈元琳
柴跃廷
刘义
徐扬
关键词 K-means聚类群体偏好交易评价可信度行为分析    
Abstract:Transaction and rating data can be used to evaluate credit as a key underlying service provided by online transaction platforms. However, credit evaluation methods pay little attention to user rating preferences, so rating manipulations can expand arbitrarily. A method is given here to determine transaction rating credibility based on group preferences. This method uses the K-means clustering algorithm to divide all the users into three groups and analyzes and validates each group's rating preferences. Then, the algorithm provides three steps to determine the credibility of ratings on different levels for different user groups based on these preferences. This paper also provides a strategy to dynamically update the group division and credibility to effectively restrict credit manipulation.
Key wordsK-means clustering    group preference    transaction rating    credibility    behavior analysis
收稿日期: 2014-12-31      出版日期: 2015-08-04
ZTFLH:  TP391.1  
通讯作者: 柴跃廷,教授,E-mail:chaiyt@tsinghua.edu.cn     E-mail: chaiyt@tsinghua.edu.cn
引用本文:   
陈元琳, 柴跃廷, 刘义, 徐扬. 基于群体偏好的交易评价可信度[J]. 清华大学学报(自然科学版), 2015, 55(5): 558-564,571.
CHEN Yuanlin, CHAI Yueting, LIU Yi, XU Yang. Transaction rating credibility based on user group preference. Journal of Tsinghua University(Science and Technology), 2015, 55(5): 558-564,571.
链接本文:  
http://jst.tsinghuajournals.com/CN/  或          http://jst.tsinghuajournals.com/CN/Y2015/V55/I5/558
  表1 部分前100用户评分个数表
  表2 部分前100用户综合加权评分等级比率
  图1 聚类4组各级评价比例曲线图
  图2 聚类3组各级评价比例曲线图
  表3 各组各级评价比例平均值
  图3 各组各级评价比例平均值曲线
  表4 全体评价与各级评价概率因子
  表5 用户群组评价可信度权重系数表
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