Title: Driving factors analysis model of social e-commerce platform users' shopping intention based on regression analysis method

Authors: Jiangdai Li; Changyi Jin; Jing Zeng

Addresses: Department of Electronic Information Engineering, Henan Polytechnic Institute, Nanyang, 473000, China ' Department of Electronic Information Engineering, Henan Polytechnic Institute, Nanyang, 473000, China ' FanLi Business College, Nanyang Institute of Technology, Nanyang, 473000, China

Abstract: In order to analyse the driving factors of shopping intention of social e-commerce platform users and improve the effectiveness of user shopping intention analysis, this article proposes a regression analysis-based model for analysing the driving factors of shopping intention of social e-commerce platform users. Firstly, the improved synthetic minority oversampling technique (BSMOTE) algorithm is used to sample process the user shopping intention data. Secondly, singular value decomposition (SVD) is used to analyse the data set and shopping intention topics. Then, regression analysis is used to quantitatively describe the driving factors and user shopping intentions, eliminate interfering factors, and construct a model for analysing the driving factors of user shopping intention. The sum of squares decomposition formula is used to modify the model and complete the model construction. Experimental results show that the proposed method can effectively analyse the factors, with an accuracy rate of 95.16% and a recall rate of over 95%.

Keywords: regression analysis method; BSMOTE algorithm; singular value decomposition; SVD; quantitatively describe; modify the model.

DOI: 10.1504/IJNVO.2023.135956

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.400 - 418

Received: 29 Apr 2023
Accepted: 06 Aug 2023
Published online: 10 Jan 2024 *

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