# A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
## 2 Related Work  
### Recommendation Systems  
At present, recommendation system based on knowledge graphs can be divided into three types: embedding-based methods, path-based methods, and graph-based methods.  
The embedding-based method uses KGE to map the items and relationships in the knowledge graph to a low-dimensional vector space, and to design a model for learning the features of users and items. The Deep Knowledge-aware network (DKE) [12] first pre-trains the entities in the knowledge graph and then uses a VGG network [13] to extract the features of the joint vector of the entity and the words in news headlines. This method uses a simple framework to integrate the entities of the knowledge graph with news recommendations. However, this method only uses the words in the title to make recommendations, so the corresponding context information is missing. Collaborative knowledge base embedding for recommender (CKE) [14] 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 in the recommendation system.  
Figure 1: Illustration of the user-item interaction knowledge graph. The users’ historical interaction records can be used to construct a user-item interaction knowledge graph. The interaction behavior can be regarded as the semantic relationship between nodes.  
Multiple paths connect the nodes in the knowledge graph. These paths can provide more additional information and interpretability for the recommendation system. The path-based method uses the path information in the knowledge graph to enhance the recommendation system and provide explanations for the recommendation results. Explainable Reasoning over Knowledge Graphs for Recommendation (KPRN) [15] uses a long short-term memory (LSTM) network [16] to capture the dependencies and inference paths of items to infer user preferences and generate reasonable explanations. Although the model based on path traversal provides interpretability and a good performance improvement, it consumes a large amount of computing resources when learning the optimal path. RuleGuider [17] proposed a rule guider to learn the probability distribution of the reasoning path. RuleGuider uses a symbol-based method to mine high-quality rules, and introduces an agent in reinforcement learning [18] to learn. The walking path uses high-quality rules to provide reward supervision for the agent.  
Graph-based methods focus on the association between nodes and neighbors in the knowledge graph. A common approach is to treat a specific entity node as a center for aggregation to capture the node's characteristics and neighbors. Knowledge Graph Convolutional Networks for Recommender Systems (KGCN) [19] uses a graph convolutional network to aggregate neighbor nodes to obtain the k-hop structure information of the central node. Simultaneously, it weights neighbors according to the connection relationship and specific user scores to represent the semantic information of the knowledge graph. Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph (NACF) [20] uses the attention in the graph convolutional network (GCN) [21], calculates the weight for historical interactive items, and then uses the graph convolutional network to aggregate the items to capture the user's preferences.