# Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
## 1 Introduction
GAT introduced an attention mechanism. When calculating the representation of each node in the graph, it will assign different weights according to the different features of the neighbor nodes. There are three methods to introduce the knowledge graph into the recommendation system. (1) Path-based method. This uses the connection information of entities in the KG for recommendation and enhances the recommendation results by the similarity between users and items. It considers KG as a heterogeneous information network, using semantic similarity in different meta-paths to refine the representation of users and items. The path-based method uses KG in a more intuitive way but relies on manual design. (2) KG Embedding. This method uses the relationships in the KG to enrich the representation of items and users requiring the use of a KG into a low-dimensional dense vector representation. Representative models: DKN [19], MKR [12]. DKN regards entity embeddings and word embeddings as different channels, then designs a CNN framework to combine together for news recommendation. The embedding-based method has high flexibility in utilizing KG to assist the recommendation system, but this method is more suitable for in-graph applications, such as knowledge graph completion, rather than recommendation. (3) A hybrid method combining knowledge graph embedding and path. The path-based method uses semantic connection information for recommendation, and the embedding-based method uses semantic representation of users or items in the KG for recommendation. Both methods only use one aspect of the information in the graph, so a hybrid method is proposed based on the idea of embedding propagation. First, the users' preferences and interests are obtained in a transfer method on the KG, and the vector representation of user's interest preference is performed by using graph embedding technology, and then the embedding of multi-hop neighbors of the aggregated items is used to refine the representation of items. Representative models: Ripple Network [20], KGAT [21],KGCN [22]. RippleNet trains a relationship matrix to assign weights to neighbors in the KG to propagate information, but this method has fewer sources of user information. To address the limitations of existing methods, we propose a KGDI recommendation algorithm based on KG, which is a structure-based recommendation model that integrates KG features with the graph neural network. The core idea is that when calculating the given entity representation in KG, the weight of neighbor node aggregation depends on the relationship between them and specific users, and also depends on the influence of different neighbor nodes on the central entity, which mainly reflects the following advantages: (1) the addition of specific users can better reflect the personalized interests and improve the accuracy of the recommendation results and (2) aggregation of node feature information on the local neighborhood of the node to calculate the embedding representation of each node, which can better capture the structure of the KG.