Article: Dynamic modelling of consumer purchase intentions based on fine-grained user behaviour sequences Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.34 pp.17 - 35 Abstract: With the rapid growth of e-commerce, accurately predicting consumers' purchase intentions is crucial for effective marketing. Existing approaches primarily rely on static or coarse-grained features, which fail to capture the dynamic and complex nature of user decision-making. This paper proposes a dynamic modelling framework based on fine-grained user behaviour sequences. It employs time-aware sequence encoding and a dynamic interest state network to learn and update the user's purchase intention probability in real-time. This integrated strategy captures both short-term local patterns and long-term global dependencies within behavioural sequences. Experiments on public datasets demonstrate that our model achieves a 1.52% improvement in the area under the receiver operating characteristic curve and a 2.18% gain in F1-score over strong baselines, with statistically verified improvements. This study provides novel theoretical and methodological insights for understanding and predicting dynamic customer behaviour. Inderscience Publishers - linking academia, business and industry through research

Title: Dynamic modelling of consumer purchase intentions based on fine-grained user behaviour sequences

Authors: Qianqian Zhuang

Addresses: School of Foreign Languages, Linyi University, Linyi, 276000, China

Abstract: With the rapid growth of e-commerce, accurately predicting consumers' purchase intentions is crucial for effective marketing. Existing approaches primarily rely on static or coarse-grained features, which fail to capture the dynamic and complex nature of user decision-making. This paper proposes a dynamic modelling framework based on fine-grained user behaviour sequences. It employs time-aware sequence encoding and a dynamic interest state network to learn and update the user's purchase intention probability in real-time. This integrated strategy captures both short-term local patterns and long-term global dependencies within behavioural sequences. Experiments on public datasets demonstrate that our model achieves a 1.52% improvement in the area under the receiver operating characteristic curve and a 2.18% gain in F1-score over strong baselines, with statistically verified improvements. This study provides novel theoretical and methodological insights for understanding and predicting dynamic customer behaviour.

Keywords: fine-grained behavioural sequences; dynamic modelling; purchase intent prediction; deep learning; sequence analysis.

DOI: 10.1504/IJICT.2026.153001

International Journal of Information and Communication Technology, 2026 Vol.27 No.34, pp.17 - 35

Received: 10 Dec 2025
Accepted: 10 Jan 2026
Published online: 17 Apr 2026 *

Title: Dynamic modelling of consumer purchase intentions based on fine-grained user behaviour sequences

Keep up-to-date