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

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.