Personalized wine recommendations in e-commerce: Integrating knowledge graphs and graph neural networks

This study proposes a wine recommendation system based on heterogeneous graph transformers that integrates hybrid node features from users, reviews, and wine knowledge. We use real-world data from a wine e-commerce platform, including 1685 wines, 12,361 users, 71,507 reviews, and 71,507 ratings. A multi-relational knowledge graph is constructed to represent relationships among wines, customers, and content. Node features are generated via decoding-enhanced BERT with disentangled attention–based review embeddings and structured wine attributes, while the graph structure captures both objective and subjective signals. We evaluate three heterogeneous graph transformer variants and benchmark them against the baseline light graph convolution network model. Our best-performing configuration, heterogeneous graph transformer model C with hybrid features, achieves an average improvement of 2.11% in precision and 5.87% in normalized discounted cumulative gain (NDCG) across top-K recommendations. These results highlight the value of feature diversity and graph-based modeling for enhancing personalized product recommendations in e-commerce.