%0 Journal Article %A SUN Wei %A CHEN Pinghua %A XIONG Jianbin %A SHEN Jianfang %T Graph Attention Recommendation Model Based on Dual-End Knowledge Graph %D 2022 %R 10.3778/j.issn.1002-8331.2103-0248 %J Computer Engineering and Applications %P 141-147 %V 58 %N 20 %X In view of the existing graph neural network(GNN) in capturing knowledge graph(KG) information and further using it for recommendation, focusing on the problems of project-side modeling, this paper proposes graph attention recommendation model based on dual-end knowledge graph. This model effectively enhances recommendation by mining relevant attributes on KG from the user side and the item side. From the perspective of the user side, it spreads user interest through the connections between entities in the knowledge graph and expands the users potential interest along the users historical clicks in KG. From the project side perspective, by capturing high-level structure and semantic information in KG, it samples the neighbors of each entity as the receiving field. The method obtains entity-entity interaction information through graph attention, and models high-order neighborhood information, finally uses the cross-entropy loss function for training. The results show that the model proposed in this paper is superior to other advanced approaches in accuracy and reliability on the three data sets about movie, book and music recommendation. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2103-0248