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Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform

机译：基于Weibo平台的行为数据的AHP和模糊综合评估信用评分

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It is increasingly necessary to evaluate the customers' credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users' credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users' behavioural characteristics. We found that the three key indexes determining the user’s social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods.
机译：越来越必要评估客户的信用。在大数据的时代，有关互联网的信息通常用于判断客户的信誉。有些用户的信用信息不完整或不可用，因此信贷管理人员无法判断这些用户的真实信用状况。然而，随着社会数据尤其是行为数据和信用评估系统的支持，可以有效解决这个问题。本研究使用了魏博来获得中国用户的行为数据进行信用评估。两种方法用于计算Weibo用户的信用评分，这些方法是分析层次处理（AHP）和模糊综合评估方法。通过分析社会流程并邀请专家做出决策，我们构建了一个信用评估系统，以暴露用户的行为特征。我们发现，确定用户的社交信用的三个关键指标是个人识别，行为特征和朋友之间的互动。然后，使用AHP来确定每个索引的权重。最后，提出了一种使用模糊综合评估方法计算微博用户的信用评估系统的静态算法。

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