Personalized wine recommendations in e-commerce: Integrating knowledge graphs and graph neural networks
A wine recommendation system based on heterogeneous graph transformers that integrates hybrid node features from users, reviews, and wine knowledge and achieves an average improvement of 2.11% in precision and 5.87% in normalized discounted cumulative gain (NDCG) across top-K recommendations is proposed.