# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
###### Abstract
[12] proposed the PER model, which uses the heterogeneous network structure of a KG to characterize the connectivity of users and items, in terms of the potential features of the different types of relationships extracted. It is important to note that PER relies heavily on the design of meta-paths, a design that requires significant domain expertise, which limits the application of PER in general-purpose scenarios. [PERSON] et al. [13], proposed the collaborative knowledge embedding model, that combines collaborative filtering with entity embedding in the KG, but the knowledge graph embedding (KGE) module in its model, is better for entity completion or link prediction in the KG, and it is difficult to achieve feature interaction between the explicit semantics  
Figure 1: A movie recommendation knowledge graph. of the KG and the implicit semantics of the user. [PERSON] et al. [10] designed a RippleNet model, which combines entity embedding with entity path connectivity, introducing the concept of user preference propagation in a KG, for the first time. However, RippleNet relies heavily on the representation of attributes, which largely weakens the importance of edges in the KG. In addition, the set of user preferences will change in unknown ways as the size of the KG increases, and most likely cause a large memory overhead. [PERSON] et al. [14] also designed and proposed a multi-task learning framework based on KG and MKR, which allows the user preference prediction recommendation task and the KGE task to be performed separately, and uses a specially designed interaction unit to associate the two tasks. However, MKR ignores the richer information of users and items in KG, resulting in the insufficient embedding of users and items in the recommendation system. To address the limitations of existing KG-based recommendation algorithms, inspired by MKR, a knowledge-aware enhanced network combining neighborhood information for recommendation (KCNR), is proposed, which is an end-to-end model. KCNR mainly consists of a recommendation module, an information complementary module (ICM), and a KGE module. In the recommendation module, KCNR firstly combines the prior information of user interaction items with the rich association semantic information in the KG, and then obtains and aggregates the weights of different related entities in the KG through a knowledge-aware attention network, so as to enrich the user's embedding representation. Similarly, KCNR uses the idea of a graph convolutional network [15] to sample the neighborhoods of item entities in the KG, and bias aggregates the item neighborhood information to enrich the embedding representation of the original items. This bias mainly refers to the use of connectivity relations and user-specific generated scores in the KG, to weight the aggregated item neighborhood information. Through the above operations, we can not only characterize the associated semantic information in the KG, but also capture the personalized interest of users in the relationship. In addition, KCNR also designs an ICM, to combine the recommendation module and the KGE module. The main idea of the ICM is to exploit the one-to-many relationships between item entities in the KG, and to explicitly model enhanced item representations and higher-order interaction features between entities, using cross-compression awareness to automatically control the knowledge transfer between the two. This also brings regularization to the model, to a certain extent. The contribution of KCNR can be summarized in three aspects as shown below:  
1. We propose KCNR, a knowledge-aware enhanced network combining neighborhood information, which is an end-to-end model. KCNR effectively utilizes users' prior information and the rich associative semantic information in the KG, and greatly enhances user and item representations, by designing a user representation layer (URL) and item representation layer (IRL), to alleviate the data sparsity problem of the RS. 2.