# Research of Personalized Recommendation Technology Based on Knowledge Graphs
###### Abstract  
Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs' construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation. k +
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or e-commerce website, the scale of users and items is huge. Meanwhile the interaction information between them is very limited. Therefore, the problem of data sparsity arises. The recommendation algorithm has been continuously improved, resulting in the recommendation model based on neural network, such as DeepFM, Wide&Deep, etc. They cannot only effectively improve the sparse problem of user item matrix in collaborative filtering algorithm, but also can automatically combine features, greatly improving the effect of recommendation [1]. At present, Tencent video, Taobao, TikTok and other websites have been using neural networks to optimize their recommendation models, in order to increase click-through rate and stay time to bring greater benefits. The purpose of knowledge graphs is to show entities, events and their relations in the real world in a structured form, and to present Internet information in a way that people can easily understand. Its essence is a knowledge base with a directed graph structure [2]. In order to improve the effect of recommendation, more and more researchers begin to study the recommendation model based on knowledge graphs. This recommendation method not only integrates the external rich semantic information of item entities, but also introduces the association information between items, which can effectively alleviate the problem of data sparsity and cold start [3]. At the same time, through the structural display of knowledge graphs, various complex relations between projects can be displayed vividly. Therefore, the hybrid recommendation model that introduces the knowledge graphs has become a trend of current research [4]. This paper works on the knowledge graphs of the movie field and constructs the knowledge graphs. Furthermore, we mine the data of entities and their relations. We use different methods to optimize the movie knowledge graphs. Furthermore, we improve the knowledge graph representation learning method. Combined with Bayesian personalized recommendation and deep neural network, the movie recommendation model is constructed. It provides a new solution for the application of knowledge graphs and the recommendation model.