# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 1 Introduction
Users' interests are centered on their history records and, layer-by-layer, extend to other items or entities on the knowledge graph to achieve the propagation of users' interests. However, RippleNet is unable to distinguish the importance of items or entities within the propagation range. [PERSON] et al. proposed the Knowledge Graph Convolutional Network (KGCN) [34], a recommendation method based on graph convolutional neural networks that can automatically capture higher-order structural and semantic information in the knowledge graph. Unlike RippleNet, which focuses more on the expansion of user history, KGCN focuses more on the expansion of item entities. Since users have different purchase intentions and items have different attributes, it is easy to ignore different aspects of user and item information, so the obtained embedding representation is suboptimal. Traditional recommendation algorithms assume that all user-item interactions are undifferentiated, which leads to learned user representations that contain only coarse-grained user-item interaction intent, i.e., the intent is coupled. Existing knowledge graph-based recommendation algorithms suffer from the problem that when modeling users using their histories, they do not mine information at the item granularity and cannot distinguish the importance of different items in the histories. In response to the above limitations of existing recommendation algorithms, this paper proposes an end-to-end KG-based recommendation model named AKGP (attentive knowledge graph perceptual propagation for recommendation). The framework uses knowledge graphs as auxiliary information to obtain the embedded representations of  
Figure 1: An example of car manufacturing scheduling recommendation with KG. Considering different models of cars as users, Car1 often uses E1, E2 and E3 on the production line, and commonly uses M2 and M3, similar to the production line situation of Car3. When the production demand of Car1 suddenly increases, the recommendation system will suggest transferring some production tasks to the production line of Car3 to reduce the pressure. users and items, and through a multilayer propagation method, the extended entity sets and triples with different distances from the initial entity sets are obtained; these can effectively extend the potential vector representations of users and items. A knowledge-aware attention mechanism is used to generate different attention weights for tail entities to reveal their different meanings when different head entities and relationships are available. Knowledge graph embedding representations from propagation layer users and items are aggregated, and the predicted user preferences are output by the inner product. The model is further optimized using a stratified sampling strategy to retain entity features while reducing sample bias. In summary, the contributions of this paper are as follows. * This article proposes an end-to-end algorithmic framework, AKGP, that uses knowledge graphs as auxiliary information, fusing structured knowledge with contextual information to build fine-grained knowledge models. * This article proposes an attention network that emphasizes the influence of different relationships between entities and explores users' higher-order interests through perceptual propagation methods. Moreover, a stratified sampling strategy is used to retain entity features and reduce sample bias. * Extensive experiments are conducted on three datasets, and the experimental results demonstrate the effectiveness of the model in solving the sparsity and cold-start problems.