Prediction of Online Shoppers' Purchasing Intention Based on SHAP-IGWO-EM Method

Predicting online shoppers' purchasing intention is crucial for e-commerce management. This study proposes SHAP-IGWO-EM, a hybrid ensemble framework to enhance prediction accuracy and interpretability for high-dimensional, imbalanced behavioral data. The method integrates XGB, LGBM, and GBM within a stacking architecture, optimized via an enhanced Grey Wolf Optimizer featuring a nonlinear convergence factor, improved leader selection, and Lévy flight perturbations to escape local optima. SHAP analysis identifies key features, reducing redundancy and enabling transparent model interpretation. Experiments on a public e-commerce dataset demonstrate SHAP-IGWO-EM's superior performance over baseline and ensemble models. Ablation studies validate the optimization strategy and SHAP-based feature selection. By uncovering critical drivers of purchase behavior, the model not only boosts predictive power but also provides actionable insights for personalized marketing and operations.