# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 1 Introduction  
Currently, along with the rapid development of the Internet and big data technology, users are facing the question of information overburden. To alleviate information overburden, recommendation systems bring into play an irreplaceable function in applications that provide information services, such as e-commerce, social platforms, and news media [1]. Among the existing recommendation models, the collaborative filter (CF) recommendation model [2; 3; 4; 5] is the most widely used model in the recommendation model, which holds that the user's interactive items express the user's direct preference information; the interactive users of the items express the feature information of the items. Based on this, the collaborative filtering recommendation model is organized into item-based and user-based. Item-based methods calculate the similarity matrix between items to measure the similarity between the item and the items in the user interaction history to estimate the user's preference for the item; user-based methods count the similarity matrix between users to find users similar to the current user and then recommend according to the interaction history of similar users. Although the recommendation model based on CF has achieved great success in a large number of application scenarios, it still faces the questions of sparse data [6; 7; 8] and cold start [9; 10; 11; 12]. The data sparsity refers to the fact that user and item interaction information is often very scarce. For example, in movie recommendation, there are often thousands of movies, but users often rate only a few dozen movies. Usingso little data to predict a large amount of unknown information greatly increases the risk of overfitting. The cold start means that there is no corresponding historical information for newly added users or items, so it is difficult to make accurate recommendations. A knowledge graph (KG) includes abundant item attribute information and correlation information; among them, the basic structure of the knowledge graph is a directed heterogeneous graph. The nodes in the graph correspond to the entities, and the edges correspond to the semantic relationships between entities. Further, (\(h\), \(r\), \(t\)) represents the knowledge graph triple; \(h\in\varepsilon\), \(r\in\mathbb{R}\), \(t\in\varepsilon\) represent the head entity, relation, and tail entity in the triple, respectively; \(\varepsilon\) and R represent the set of entities and relations in the knowledge graph, respectively; and the knowledge graph is brought into the recommendation system as assistant information, which can alleviate the questions of sparse data and cold start. Therefore, recommendation models based on knowledge graphs have received increasing attention from researchers. [PERSON] et al. proposed the Ripple Net [13], which takes the item as the center, spreads outward along the relationship in the KG to aggregate the information of the surrounding nodes, and then constructs the feature vectors of users and items. [PERSON] et al. proposed the KGCN [14], which introduces a graph convolutional network into the knowledge graph recommendation field. KGCN combines the entity node's own information and neighborhood node information to calculate the entity embedding vector containing high-order correlation information. [PERSON] et al. proposed the KGAT [15], which integrates the user-item bipartite graph (UIG) and the knowledge graph (KG) into a collaborative knowledge graph (CKG). KGAT assembles the neighborhood information of nodes through the attention mechanism on the CKG and excavates the high-order correlation information between entities by stacking multilayer network structures to construct the embedded vector representations of users and items. [PERSON] et al. proposed the CKAN [16], which obtains the feature vector of the item through the attention mechanism embedded in the content feature in the KG.