Title: The deep mining of consumer behaviour data on product network marketing platform

Authors: Chunming Yu; Xin Jin

Addresses: Department of Information Management, Shanghai Lixin University of Accounting and Finance, Songjaing District, Shanghai, China ' Marketing Department, AIWAYS Automobile Co., Ltd., Shanghai, China

Abstract: To overcome the problems of low accuracy of behaviour analysis results and low purchase proportion in traditional methods, a deep mining method of consumer behaviour data on product network marketing platform is proposed. Firstly, according to the nearest neighbour data distribution, the relevant subspace of consumer behaviour data of product e-marketing platform is divided, and the sparsity difference of relevant subspace data in each attribute is calculated. Then, the filtering of outlier interference data is completed by setting the difference threshold of local sparsity factor. Finally, the multi-source data mining method is used to analyse the difference between the data attribute weight and the target weight of each attribute behaviour, so as to realise the in-depth mining of consumer behaviour data. Test results show that the maximum error of consumer behaviour analysis of the design method is only 1, and the purchase proportion after secondary marketing reaches 10.2%.

Keywords: network marketing platform; consumer behaviour data; deep mining; nearest neighbour data; local sparse factor; outlier data.

DOI: 10.1504/IJPD.2024.137817

International Journal of Product Development, 2024 Vol.28 No.1/2, pp.13 - 23

Received: 02 Apr 2022
Accepted: 03 Oct 2022
Published online: 05 Apr 2024 *

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