Abstract
Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.
Highlights
In recent years, with the development of the Internet, online services in all walks of life came into being
In the environment where big data technology is widely used, in order to meet the following challenges, each platform organization uses the data collected by the platform to calculate credit indicators and build its own credit evaluation system
The outline of the plan for the construction of social credit system issued by the State Council (2014-2020) puts forward that “accelerating the construction of credit information system and improving the recording, integration and application of credit information are the basis and premise for the formation of trustworthy incentive and dishonest punishment mechanism.”
Summary
With the development of the Internet, online services in all walks of life came into being. The outline of the plan for the construction of social credit system issued by the State Council (2014-2020) puts forward that “accelerating the construction of credit information system and improving the recording, integration and application of credit information are the basis and premise for the formation of trustworthy incentive and dishonest punishment mechanism.”. From this point of view, to solve the data problem in credit evaluation, it is necessary for all platforms. The core content of building a crossplatform credit index evaluation model is to fuse multisource heterogeneous credit data, and the information conflict caused by data fusion is the focus of the research: there are attribute differences between the same information and different names between the data attributes of each platform, there are numerical differences between the same attributes of different sources, and due to the data collection methods to sum up, the purpose of conflict detection modelling is to match attributes, solve conflict problems, clean up false data, and obtain data with unified standards, reliable sources, and strong authenticity, so as to achieve high efficiency and authenticity in the construction of subsequent credit models
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