Article: E-commerce consumer behaviour prediction through the integration of collaborative filtering and graph neural networks Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.34 pp.134 - 150 Abstract: Due to the huge amount of information interested by users on e-commerce platforms, it is difficult to predict consumers' purchasing behaviour. To this end, this paper first forms a session graph based on consumers' session sequences. Meanwhile, inter-item multivariate relationships and inter-session cross-information are modelled through graph convolutional networks. Then, the user's intention representation is generated through comparative learning. Next, a behavioural model of user consumption based on attention network is constructed. Finally, this paper calculated the ratings of users with purchasing behaviours on the target items, and obtained several items with high ratings to generate a recommendation list to predict e-commerce consumers' behaviours. Experiments are conducted on two public datasets, and the results show that the accuracy of the proposed model is improved by at least 3.31% and 5.21% respectively, which effectively improves the accuracy of e-commerce consumer behaviour prediction. Inderscience Publishers - linking academia, business and industry through research

Title: E-commerce consumer behaviour prediction through the integration of collaborative filtering and graph neural networks

Authors: Shuxin Wei

Addresses: School of Management, Guangdong University of Science and Technology, Dongguan, 523083, China

Abstract: Due to the huge amount of information interested by users on e-commerce platforms, it is difficult to predict consumers' purchasing behaviour. To this end, this paper first forms a session graph based on consumers' session sequences. Meanwhile, inter-item multivariate relationships and inter-session cross-information are modelled through graph convolutional networks. Then, the user's intention representation is generated through comparative learning. Next, a behavioural model of user consumption based on attention network is constructed. Finally, this paper calculated the ratings of users with purchasing behaviours on the target items, and obtained several items with high ratings to generate a recommendation list to predict e-commerce consumers' behaviours. Experiments are conducted on two public datasets, and the results show that the accuracy of the proposed model is improved by at least 3.31% and 5.21% respectively, which effectively improves the accuracy of e-commerce consumer behaviour prediction.

Keywords: e-commerce consumer behaviour prediction; collaborative filtering; graph convolutional network; GCN; attention network; comparative learning.

DOI: 10.1504/IJICT.2025.148826

International Journal of Information and Communication Technology, 2025 Vol.26 No.34, pp.134 - 150

Received: 16 Jun 2025
Accepted: 02 Jul 2025
Published online: 26 Sep 2025 *

Title: E-commerce consumer behaviour prediction through the integration of collaborative filtering and graph neural networks

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