Title: Research on purchasing behaviour prediction of e-commerce platform users based on multidimensional data mining

Authors: Juan Long

Addresses: Chongqing City Vocational College, Chongqing 402160, China

Abstract: In order to overcome the problems of low accuracy of data mining, high relative error of prediction and high time consumption of traditional methods, a purchasing behaviour prediction method of e-commerce platform users based on multidimensional data mining is proposed. FP-Growth algorithm is used to mine the multidimensional data of e-commerce platform users' purchasing behaviour, so as to extract the characteristics of users' purchasing behaviour. Combined with feature extraction results, the hidden Markov model is used to calculate the probability of user access and the probability of no access. If the probability of access is greater than the probability of no access, it is considered that the user will access in the next month, otherwise it will not. The experimental results show that the average data mining accuracy of this method is 96.39%, the maximum prediction relative error rate is 5%, and the time consumption is always below 0.8 s.

Keywords: multidimensional data mining; e-commerce platform; user purchase behaviour; behaviour prediction; hidden Markov model.

DOI: 10.1504/IJWBC.2023.134867

International Journal of Web Based Communities, 2023 Vol.19 No.4, pp.305 - 319

Received: 29 Dec 2021
Accepted: 06 May 2022
Published online: 15 Nov 2023 *

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