Title: Prediction method of e-commerce consumers' purchase behaviour based on social network data mining

Authors: Ming Yang

Addresses: School of Business, Chongqing College of Electronic Engineering, Chongqing 401331, China

Abstract: In order to effectively improve the prediction accuracy of e-commerce consumers' purchase behaviour and shorten the prediction time of e-commerce consumers' purchase behaviour, a prediction method of e-commerce consumers' purchase behaviour based on social network data mining is proposed. Firstly, according to the statistical characteristics of e-commerce consumers' purchase behaviour, data mining method is used to extract the characteristics of e-commerce consumers' purchase behaviour. Secondly, the social network analysis method is used to analyse the purchase behaviour characteristics of e-commerce consumers and the social network model. Finally, build the prediction model of e-commerce consumers' purchase behaviour to realise the prediction of e-commerce consumers' purchase behaviour. The experimental results show that the proposed method has a good effect on the prediction of e-commerce consumers' purchase behaviour, and can effectively improve the prediction accuracy of e-commerce consumers' purchase behaviour. The prediction deviation rate is only 1.8%.

Keywords: social network analysis method; data mining methods; e-commerce consumers; purchase behaviour; prediction model.

DOI: 10.1504/IJWBC.2024.136648

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.102 - 113

Received: 03 Mar 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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