HKGAT: heterogeneous knowledge graph attention network for explainable recommendation system

Abstract

This paper presents the Heterogeneous Knowledge Graph Attention Network (HKGAT) for recommendation systems. As recommendation technology evolves, systems now emphasize diversity, fairness, and explainability alongside accuracy. Traditional methods encounter issues integrating knowledge graphs and lack explainability. HKGAT addresses these by leveraging heterogeneous knowledge graphs. It consists of a heterogeneous information aggregation layer, an attention-aware heterogeneous relation fusion layer, and a prediction layer. First, recommendation data forms a user-item knowledge graph. Then, the aggregation layer collects relation information, followed by the fusion layer integrating it for higher-order feature representations. The prediction layer combines link prediction and recommendation score prediction. Additionally, paths of top-ten results are analyzed and quantified for explainability to optimize ranking. Experiments on self-constructed and Amazon-book datasets show HKGAT outperforms baselines like HetGCN, with significant improvements in Precision, Recall, F1 score, and NDCG@10, and a notable 1.9% gain in NDCG@10 from explainable ranking optimization.