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