Title: A method for predicting consumer purchase intention in e-commerce in the era of social media

Authors: Aihua Mo

Addresses: School of Management, Hunan City University, Hunan, 413000, China

Abstract: The research goal is to improve the AUC value and R² value of the prediction results and reduce the average absolute error and propose a prediction method of e-commerce consumers' purchase intention in the era of social media. Firstly, linear transformation is performed on the historical purchasing data of consumers, and the importance of the data is determined through thresholds. Based on the judgment results, the data is dimensionally reduced. Secondly, the minimum hash algorithm is used to calculate the similarity between the dimensionality reduced data, and the fuzzy clustering decision method is used to classify historical purchase data. Finally, the Bayesian personalised sorting method is used to predict the purchase intention. The experimental results show that the AUC value and R² value of the proposed method are large, and the average absolute error is low, indicates that the prediction effect of this method is good.

Keywords: the era of social media; electronic commerce; purchase intention prediction; minimum hash; Bayesian personalised sorting method.

DOI: 10.1504/IJNVO.2023.135957

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.386 - 399

Received: 28 Apr 2023
Accepted: 06 Aug 2023
Published online: 10 Jan 2024 *

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