# Research of Personalized Recommendation Technology Based on Knowledge Graphs
## 2 Related Works
At the same time, many model-based recommendation algorithms have been produced, such as the collaborative recommendation algorithm Apriori based on the association algorithm and the collaborative recommendation algorithm NCF based on the neural network [11]. At present, recommendation algorithms have evolved from classic collaborative filtering algorithms to methods that combine deep neural networks, such as DeepFM, Deep&Wide and Alibaba's XDeepFM model, which is currently used in the majority of e-commerce websites. The recommendation algorithm based on a neural network can automatically perform feature interaction. Furthermore, various features can be continuously and autonomously learned and trained in the neural network to achieve better results [12]. In the field of movie recommendation, YouTube's recommendation technology has been far ahead. Figure 1 is the framework diagram of the movie recommendation model used by YouTube. It uses Word2 Vec, ReLU, softmax, etc., to generate candidate sets. In the recall phase, the value of the linear regression function is used as the sorting criterion to recommend the highest ranked video for users. Furthermore, it can finally achieve good results. This model is also a classic recall model in the history of recommendation algorithm development. Nowadays, more and more companies apply recommended technology to Internet products. For example, the homepage of the current popular short video platform is a feed stream composed of recommended models, which effectively increases the length of stay and frequency of users. The application of the recommendation system has brought great convenience to people. However, these recommended technologies also have many problems such as data sparseness and cold start. Recommendation system based on knowledge graphs: knowledge graphs have natural advantages in expanding entity information and strengthening the relations between entities. It can provide a powerful and rich reference for recommendation systems. At the beginning, the researchers directly applied the entity attributes in the knowledge graphs to the recommendation algorithm. Then they produced feature-based knowledge graphs assisted recommendation, that is, the introduction of knowledge graphs representation learning. This method can provide deeper and longer-range associations between entities. Subsequently, a structure-based knowledge graph recommendation model was produced. The structure-based recommendation model can use the structure of the knowledge graphs  
Figure 1: Frame diagram of YouTube recommendation model. more directly. For example, it can perform a breadth-first search on each entity to obtain multi-hop related entities nearby, and get recommendation results from it [13]. In 2016, [PERSON] and other researchers proposed the CKE (Collaborative Knowledge Base Embedding for Recommendation Systems) recommendation model, which aroused widespread concern around the world. The model uses the TransR [14] representation learning algorithm to obtain the structured information in the knowledge graphs. It uses the encoder to obtain the visual and text representation vectors of the items, and introduces them into the latent factor vectors of the items. Finally, it combines collaborative filtering for recommendation [15]. In recent years, deep learning has been extremely popular, and deep neural network recommendation algorithms based on knowledge graphs have also been widely studied. In 2018, DKN (Deep Knowledge-Aware Network for News Recommendation) proposed by [PERSON] and others for news recommendation [16] is the representative algorithm. Knowledge graph recommendation system is applied to many fields, such as smart social tv [17], personalized user recommendations [18; 19; 20], etc. However, in recent years, the movie knowledge graph has only just started. Although many scholars have studied knowledge graphs for movie recommendations [21; 22] and used graph databases for recommendations [23; 24], these studies still have many drawbacks and need to be continuously studied.