A deep mining method for consumer behaviour data of e-commerce users based on clustering and deep learning. (16th January 2023)

• Record Type: Journal Article
• Authors: Li, Jing
• Is Part Of: International journal of Web based communities. Volume 19:Number 1(2023)
• Page Start: 2
• Page End: 14
• Publication Date: 2023-01-16
• Subjects: data clustering -- deep learning -- dimension kernel function -- centroid; Online social networks -- Periodicals; Social media -- Periodicals; World Wide Web -- Periodicals; 302.30285
• Languages: English
• ISSNs: 1477-8394

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%.