In order to improve the accuracy and interpretability of the recommendation algorithm,some auxiliary information of users and items is usually added and used in the recommendation algorithm.A large number of experiments show that adding knowledge graphs as auxiliary information to the recommendation algorithm can effectively obtain the correlation between items by mining the relevant attributes between entities,thereby greatly improving the performance of the recommendation.Inspired by the graph attention neural network and KGCN,an attention embedding propagation layer was designed to calculate the neighbor information of entities in the knowledge graph to enrich project representation.Experiments were conducted on three real data sets.The analysis of the results shows that this algorithm has the best recommendation performance in movie and book recommendation;in music recommendation,it has also achieved a high recommendation effect.
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