Abstract

The problem of correctly predicting the intensity of online purchasing proves still to be very challenging due to scattered behavioural cues, extreme class imbalance and the increasing importance of trust and security in the decision-making process of consumers. Purchase prediction models currently in use have mostly been based on single behavioural measures, i.e. click rate, purchase history, etc. Single behavioural measures are not sufficient to capture the multidimensionality of purchase intention in actual e-commerce settings. To overcome these drawbacks in current paper proposes the development of Security-Conscious Composite Behavioural Intelligence (SC-CBI) model for the forecasting of online purchasing intensity at multiple levels. The proposed model introduces Composite Behavioural Indices (CBI) that introduce behavioural engagement, temporal consistency, monetary commitment and security-induced trust interactions in a low dimensional representation of user intent. In addition, a Security-Aware Balanced Classification Strategy (SABCS) will be developed in order to overcome the severe class imbalance and to preserve trust-related behavioural distributions by adaptive resampling and risk-sensitive cost optimisation. Tests on large-scale, real-world e-commerce data show that the given framework is better than traditional feature-based and imbalance-blind models. The SC-CBI model has an average accuracy of 92.7, a macro F1-score of 90.8 and a balanced recall of 90.2 which can improve up to 8-12 points compared to baselines from strong ensembles. The given framework is most appropriate for implementation in the contemporary e-commerce systems that require precision decision support in the security impaired and data imbalanced environment.