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Due to a large amount of information, it is difficult for users to find whatthey are interested in among the many choices. In order to improve users'experience, recommendation systems have been widely used in musicrecommendations, movie recommendations, online shopping, and other scenarios.Recently, Knowledge Graph (KG) has been proven to be an effective tool toimprove the performance of recommendation systems. However, a huge challenge inapplying knowledge graphs for recommendation is how to use knowledge graphs toobtain better user codes and item codes. In response to this problem, thisresearch proposes a user Recurrent Neural Network (RNN) encoder and itemencoder recommendation algorithm based on Knowledge Graph (URIR). This studyencodes items by capturing high-level neighbor information to generate items'representation vectors and applies an RNN and items' representation vectors toencode users to generate users' representation vectors, and then perform innerproduct operation on users' representation vectors and items' representationvectors to get probabilities of users interaction with items. Numericalexperiments on three real-world datasets demonstrate that URIR is superiorperformance to state-of-the-art algorithms in indicators such as AUC,Precision, Recall, and MRR. This implies that URIR can effectively useknowledge graph to obtain better user codes and item codes, thereby obtainingbetter recommendation results.

URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph