Knowledge Graph-Based Dual Feature Aggregation Recommendation Model

To overcome the shortcomings of current approaches in feature extraction, specifically the lack of in-depth feature extraction and the absence of high-order feature aggregation for both users and items, we propose a knowledge graph-based dual-end feature aggregation recommendation model. First, initial vectors are generated using knowledge graph embeddings. Next, a ripple network and a knowledge graph attention network are employed to extract features on the user and item sides, respectively. Then, features of various orders are aggregated and concatenated to obtain the final vectors. Finally, the matching probability between users and items is predicted. Tests performed on three datasets reveal that the new model surpasses seven other baseline models based on AUC, ACC, NDCG@K, and Recall@K metrics.