In transaction platform, because of competing for market share, this will inevitably bring a series of problems and risks for companies. Decision of the managers and consumers in third-party payment platform, are issue of great concern to platform development. Using rough set theory, this paper constructs the third-party payment platform transaction’s rough complex network, establishes a rough complex networks trust model for platform managers to enhance efficiency, a customer select commodities rough decision model for customer’s benefit maximization. In this paper, rough set theory is used to study the uncertainty of complex network trust model, but also given attribute reduction algorithm based on time series analysis to solve the attribute rules extraction in dynamic knowledge system. Simulation examples show that the models and algorithms provide the science decision-making approach for a direct participant of the platform transaction.
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This work was supported by the Shannxi Provincial Education Department science study plan project under grant 18JK0373. This work was supported by the Shaanxi Province to improve the public scientific quality research program project (36, Research on risk warning method of science online shopping based on the Improvement of public science quality).
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Cao, L. Research on Two Decision Models in Third-Party Payment Platform Transaction. Wireless Pers Commun 110, 141–151 (2020). https://doi.org/10.1007/s11277-019-06716-0
- Rough set
- Decision models
- Third-party payment platform
- Dynamic knowledge system