An Explainable Hypergraph Neural Network Framework for Intelligent Customer Segmentation and Purchase Behavior Prediction

Customer segmentation and purchase behavior prediction are fundamental tasks in intelligent e-commerce systems, enabling personalized marketing strategies and data-driven customer relationship management. However, conventional machine learning and graph neural network approaches primarily model pairwise interactions and often fail to capture higher-order relationships among customers, products, brands, and purchase contexts, limiting predictive performance and model interpretability. To address these challenges, this study proposes an Explainable Hypergraph Neural Network (EHGNN) framework that integrates higher-order hypergraph representation learning with post hoc explainability using SHAP. The proposed framework constructs a heterogeneous hypergraph from customer transaction data, learns informative customer embeddings via hypergraph convolution, segments customers via clustering, and predicts purchase behavior using an embedding fusion network. Comprehensive experiments were conducted to compare the proposed framework with conventional clustering algorithms, deep clustering methods, graph neural networks, and hypergraph neural networks. Experimental results demonstrate that the proposed EHGNN consistently achieved superior performance, obtaining a Silhouette Coefficient of 0.824, Davies–Bouldin Index of 0.336, and Calinski–Harabasz Index of 2815 for customer segmentation. For purchase behavior prediction, the proposed framework achieved an Accuracy of 97.30%, Precision of 97.00%, Recall of 96.80%, F1-score of 96.90%, Area Under the Receiver Operating Characteristic Curve (AUC) of 99.20%, and a Matthews Correlation Coefficient (MCC) of 0.942, outperforming all benchmark methods. These findings demonstrate that modeling higher-order customer relationships using hypergraph learning substantially improves both customer segmentation quality and purchase behavior prediction, while maintaining model transparency via explainable artificial intelligence. The proposed EHGNN framework provides an effective, robust, and interpretable solution for intelligent customer analytics and personalized decision support in modern e-commerce environments.