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.
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.
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