# Deep Interest Network Based on Knowledge Graph Embedding
## 2 Related Work  
### Recommendation Systems  
Currently, researchers divide the recommendation algorithms [1] based on knowledge graph into three categories. (1) Recommendation algorithm based on knowledge graph embedding [2]. (2) Recommendation algorithm based on knowledge graph path [3]. (3) HyBrid, which is a combination of the two [4].  
The Recommendation algorithm based on knowledge graph Embedding represents entities and relationships by the method of graph embedding, and then expands the semantic information of the original item and user representation. DKN [5] (deep knowledge-aware network) introduces the knowledge graph into the news recommendation task. It takes a candidate news and a user's click history as input, pretrains the entities in the knowledge graph, uses the VGG network [6] for feature extraction, and the output is the probability of the user clicking the news. However, this method only uses the words in the title to make a recommendation and does not effectively utilize the more valuable context information. CKE [7] (collaborative knowledge base embedding for recommender) uses three embedding methods to fit the knowledge graph, text, and visual information in the knowledge base. Then, it combines the three vectors and the implicit feedback of users for the recommendation system.  
The relationship between nodes and paths in knowledge graphs is usually many-to-many. The information contained in these paths can often be used as a supplement to enhance the effectiveness and interpretability of the recommendation system. KPRN [8] (explainable reasoning knowledge graph recommendation) combines the semantics of entities and relations to generate path representations and uses LSTM [9] (long short term memory) to effectively reason about the path by using sequential dependencies in the path, and then infer the rationale of user item interaction. At the same time, a new weight pool operation is designed to distinguish the importance of different paths in connecting users and items, so that the model has a certain degree of interpretability. The model using path traversal can obtain full interpretability and a certain performance improvement, but it often consumes a lot of computing resources when learning the optimal path. Rule Guidance [10] proposed a path-based rule mining method, which introduces reinforcement learning [11] into relational agents for learning. High-quality rules generated by symbol-based methods are utilized to provide reward supervision for the swing-based agent. This  
Figure 1: Illustration of dynamic interests.  
method improves the performance of walk-based models without losing interpretability, but it does not perform well on large-scale datasets.  
HyBrid refers to recommendation algorithms that combine graph paths with knowledge graph embeddings, and knowledge graph recommendation algorithms based on graph neural networks are also classified in this category. KGCN [12] (knowledge graph convolutional networks for recommender systems) captures the correlation between items by sampling from the neighbors of each entity in the knowledge graph and mining the associated attributes of items on the knowledge graph. The user's potential interests are captured at a distance. NACF [13] (neighborhood aggregation collaborative filtering based on knowledge graph) iteratively encodes the potential information of the knowledge graph into user features and uses the attention mechanism in GCN [14] (graph convolutional network) to consider the personalized preferences and multiple associations of users during information aggregation.