Knowledge Graph Attention Network Recommendation Algorithm Combined with User’s Perspective

At present, most knowledge graph based recommendation algorithm only focus on how to enrich the representation of the item in recommendation system, but they ignore the collaborative information of the user-item interaction from the user perspective. And they fail to combine the collaborative information with the side information of the knowledge graph at a fine-grained level. Aiming at the above problems, this paper proposes a knowledge graph attention network recommendation algorithm combined with the user’s perspective. On the item side, the model captures the high-order information in the graph by knowledge-aware attention embedding propagation. On the user side, this model devises the user perspective factors to refines the collaborative information in the user-item graph, and aggregates along the path of relational dependence in the knowledge graph. The model further enriches the representation of the user and the item to predict how likely the user would adopt the item. Finally, the experiments on the Last.FM and MovieLens-20M public datasets show that compared with the current mainstream baseline, the model has improved by 13%~22% in the Recall@K indicators and 0.6%~4.5% in the AUC and F1 indicators. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.