Title: Research on e-commerce personalised transaction processing model based on reinforcement learning

Authors: Jinling Chi

Addresses: Department of Finance and Economics, Huaibei Vocational and Technical College, Huaibei, 235000, China

Abstract: Aiming at increasing the amount of transaction processing issues with the rapid development of the e-commerce industry, this study proposes the personalised transaction processing model of e-commerce based on reinforcement learning. The DBSCAN method is used to pre-process the high-dimensional and low-density data in e-commerce, and the improved DR-PSO method is used to reduce the dimension of the data so as to obtain the optimal data set. Then, an e-commerce transaction processing model is constructed based on learning algorithm and the distributed multi-objective synthetic TOPE algorithm. The research results show that TOPE algorithm is the most economical, which is conducive to the long-term development of e-commerce. The results show that the e-commerce transaction processing system model proposed in this study has high adaptability and effectiveness. This study provides a reference for the progress of e-commerce in the era of artificial intelligence.

Keywords: reinforcement learning; artificial intelligence; electronic commerce; e-commerce; transaction processing model.

DOI: 10.1504/IJCSYSE.2023.132922

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.77 - 85

Received: 30 Oct 2022
Accepted: 28 Dec 2022
Published online: 16 Aug 2023 *

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