Transaction rating credibility based on user group preference

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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.

Keywords K-means clustering group preference transaction rating credibility behavior analysis

ZTFLH:

TP391.1

Issue Date: 15 May 2015

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	CHEN Yuanlin
	CHAI Yueting
	LIU Yi
	XU Yang

Cite this article:

CHEN Yuanlin,CHAI Yueting,LIU Yi, et al. Transaction rating credibility based on user group preference[J]. Journal of Tsinghua University(Science and Technology), 2015, 55(5): 558-564,571.

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http://jst.tsinghuajournals.com/EN/ OR http://jst.tsinghuajournals.com/EN/Y2015/V55/I5/558

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