# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
## 2 Related Work  
KG uses heterogeneous information to build a network with powerful semantic representation. In recent years, many studies have introduced KGs as auxiliary information to recommender systems and achieved good results. Combining KGs with recommender systems is also an emerging research area. There are three types of knowledge graph-based recommendation methods [16]: embedding-based methods, path-based methods, and unified methods. The main idea of the embedding-based method, is to use the embedding representation of entities in the KG, obtained with the KGE algorithm [17; 18], directly in the RS, to enrich user or item representations. [PERSON] et al. [13] proposed a CKE model, which focuses on how to exploit heterogeneous information of items to improve recommendation effectiveness. It unites item content information (e.g., text and visual), structural knowledge information, and user preference information, to learn together, significantly improving the performance of collaborative filtering recommendation methods. [PERSON] et al. [11] proposed the DKN model, which combines KGs with a convolutional neural network for news recommendation, to more accurately recommend news that may be of interest to users, by combining the connections between news at the knowledge level, and to capture the dynamic interest of users in the news, using the designed news following module. [PERSON] et al. [19] designed the KSR model, which uses the key-value memory network to incorporate KG information into recommender systems, and combines the KG information with item information to capture users' preferences, to enhance the model capabilities. [PERSON] et al. [14] designed a multi-task feature learning framework called MKR, which utilizes the correspondence between items and entities in a KG, to link the recommendation task and the entity embedding task. In the framework, the recommendation task learns representations of users and items and eventually makes predictions, while the entity embedding task learns representations of item-related entities and relationships, and both share latent features using cross-compression units. Embedding-based methods have high flexibility, but the directly learned entity embedding vectors sometimes cannot directly describe the recommendation relationship between items, and it ignores a large number of inter-entity connections in KG and lacks reasoning ability. The main idea of the path-based method, is to provide auxiliary information to the recommender system by using various connection patterns of the unique structure of the KG. For example, [PERSON] et al. [20] proposed the HeteRec model, which supplements the user-item interaction matrix, by calculating the similarity of user (item) meta-paths in the KG, so that richer representations of items can be extracted to improve the recommendation quality. [PERSON] et al. [21] designed the FMG model, which uses a meta-graph containing more rich connectivity information instead of meta-paths, incorporating complex semantic information into the model, so that the model can more accurately obtain the similarity of entities. [PERSON] et al. [22] proposed the MCRec model, which uses a context-based meta-path and a neural network model of a co-attention mechanism, in a heterogeneous information network, to build recommender systems. The path-based method can describe the information in the KG more intuitively, and using the correlation of entities in the KG brings a certain degree of interpretability to the recommendation. However, this method excessively depends on the meta-paths designed according to the domain of expertise, and needs to reconstruct the meta-paths when the recommendation scenarios or KGs change, thus limiting its usage scenarios. The above-mentioned two methods only utilize the unilateral information of the KG.