Distributed AI Community User Credit Evaluation Method
Authors: 
Xuezhu Xin, Yang Bai, Haixin Wang, Yunzhen Mou, Yingchao Ren, Jian TanAuthors Info & Claims
Pages 416 - 419
Abstract
This research proposed a distributed AI community user credit evaluation method. The existing credibility evaluation methods based on user behavior are mainly concentrated in the financial industry and new media and self-media content industries represented by Facebook, Twitter and Zhihu(in China). There is a lack of quantitative assessment to credit value in AI models and data set community platforms. Based on the theory of social credit risk, this study firstly calculates the original credit by obtaining the direct characteristics of user credit on the community platform of the AI model and the data set, and then analyzes and decomposes the source quality of the direct characteristics of credit. The identification of credit characteristics includes obtaining all review users who have posted reviews and scores in multiple consecutive evaluation periods, and determining the trustworthiness of the review users according to the self-similar algorithm. Finally it obtains the credit that truly reflects the user's contribution in the distributed AI community. This research provides a method reference for the construction of similar communities.
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Published In
June 2022
905 pages
ISBN:9781450397179
DOI:10.1145/3548608
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Association for Computing Machinery
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Published: 14 October 2022
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ICCIR 2022
ICCIR 2022: 2022 2nd International Conference on Control and Intelligent Robot
June 24 - 26, 2022
Nanjing, China
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