# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
###### Abstract  
In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user and item embeddings in existing knowledge graph-based recommendation methods, a knowledge-aware enhanced network, combining neighborhood information recommendation (KCNR), is proposed. Specifically, KCNR first encodes prior information about the user-item interaction, and obtains the user's different knowledge neighbors by propagating them in the knowledge graph, and uses a knowledge-aware attention network to distinguish and aggregate the contributions of the different neighbors in the knowledge graph, as a way to enrich the user's description. Similarly, KCNR samples multiple-hop neighbors of item entities in the knowledge graph, and has a bias to aggregate the neighborhood information, to enhance the item embedding representation. With the above processing, KCNR can automatically discover structural and associative semantic information in the knowledge graph, and capture users' latent distant personalized preferences, by propagating them across the knowledge graph. In addition, considering the relevance of items to entities in the knowledge graph, KCNR has designed an information complementarity module, which automatically shares potential interaction characteristics of items and entities, and enables items and entities to complement the available information. We have verified that KCNR has excellent recommendation performance through extensive experiments in three real-life scenes: movies, books, and music. attention network; graph neural network; knowledge graph; recommender systems +
Footnote †: journal: Medical  
can be an item (movie) or an attribute of the item (movie's director). Compared with other recommendation methods, introducing a KG into recommender systems has three advantages [10]. (1) We can use the extensive item and item attribute information in the KG to supplement the item representation and improve recommendation accuracy. (2) We can use various types of relationships in the KG to propagate user preferences and increase the diversity of recommendation results. (3) We can use the user's historical behavior in the KG for the recommendation, which brings certain interpretability to the recommender system. Figure 1 shows the KG in a movie recommendation scenario. The movie KG in Figure 1, contains related entities, such as the movie's stars, genre, and production company. We use various relationships between entities to connect them, to form the movie KG. For example, the \"genre\" relation in the figure, can associate a movie with its genre entity. Then, we recommend unwatched movies that the user is expected to like, for the user in the KG, based on the user's history of watching movies. For example, assuming that user \(u_{1}\) has watched movies A, B, and C, we can recommend unwatched movie F to the user, because movies F and A have the same movie genre and production company. A KG, introduces more associated semantic information into the recommender system, and we can dig deeper into the user's personalized preferences in the connection between items and item attributes, thereby improving the performance of the recommender system. For example, [PERSON] et al. [11] designed a DKN network, which combines different semantic and knowledge information for news recommendation tasks, through a designed convolutional neural network framework. However, DKN requires embedding entities before they can be used. This results in a lack of an end-to-end training approach. Another drawback of DKN, is that it can only make use of text-based auxiliary information, making it highly limited in its applicability to scenarios. [PERSON] et al.