Title: Cluster analysis of perceptual demands of users' internet consumption behaviours based on improved RFM model

Authors: Yuxi Zhang

Addresses: School of Economics and Management, Henan Polytechnic Institute, Nanyang 473000, China

Abstract: In order to overcome the problems of traditional clustering analysis methods, such as low accuracy, long consuming time and less demand types, a clustering analysis method based on improved RFM model is proposed in this paper. The intelligent internet of things platform is used to collect the data of users' online consumption behaviour, and the frequent patterns of the data collection results are mined according to the big data fusion method. The improved RFM model is used to obtain three parameters: users' latest consumption, user consumption frequency and consumption amount, so as to realise the clustering analysis of users' perceptual demand of online consumption behaviour. The experimental results show that with high clustering analysis accuracy and ability of consuming clustering analysis time by always less than 9.0 s, this proposed method can effectively cluster more types of user needs, suggesting that the clustering analysis effect of this method is relatively ideal.

Keywords: improved RFM model; online consumption behaviour; perceptual demand clustering; smart IoT platform; frequent patterns.

DOI: 10.1504/IJWBC.2023.128408

International Journal of Web Based Communities, 2023 Vol.19 No.1, pp.15 - 27

Received: 28 May 2021
Accepted: 25 Nov 2021
Published online: 20 Jan 2023 *