Title: A deep mining method for consumer behaviour data of e-commerce users based on clustering and deep learning

Authors: Jing Li

Addresses: Puyang Vocational and Technical College, Puyang 457000, Henan, China

Abstract: The data mining accuracy of e-commerce users' consumption behaviour is low and the data clustering effect is poor, so a deep mining method of e-commerce users' consumption behaviour data based on clustering and deep learning is proposed. The consumption behaviour data are divided into simple type, deterministic type, habitual row type and preference type through the user's web browsing log, and the features of the consumption behaviour data are extracted. The centroid and class spacing of behaviour characteristic data are obtained according to the actual distance between the behaviour characteristic data points. The behaviour data deep mining model is built based on the small wave neural network and the deep learning algorithm, and the optimal solution of the model is thus obtained by the gradient descent method, so as to realise the deep mining of the consumption behaviour data. The results show that the accuracy of the proposed method is up to 97%.

Keywords: data clustering; deep learning; dimension kernel function; centroid.

DOI: 10.1504/IJWBC.2023.128410

International Journal of Web Based Communities, 2023 Vol.19 No.1, pp.2 - 14

Received: 04 Jun 2021
Accepted: 25 Nov 2021
Published online: 20 Jan 2023 *