# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 1 Introduction  
With the advancement of Internet technology, people can obtain lots of information by using computers or cell phones. It is difficult for people to quickly access the information they seek when faced with such a large amount of information, which leads to the problem of information overload [1; 2], i.e., users cannot easily access the information they are searching for. On the other hand, providers of Internet content are facing a long-tail problem [3; 4; 5]. In response to the above problems, recommendation systems have become a solution. These systems have attracted much attention and have developed applications in academia and industry as they provide personalized items to meet users' interests and alleviate the problem of information explosion [6; 7; 8]. A recommendation system analyzes users' historical behaviors, preferences and interests to recommend content that meets their needs. It greatly reduces the time and cost of finding and selecting content and improves user experience and satisfaction. At the same time, it also provides Internet content providers with a more accurate target audience and higher revenue. Early recommendation systems were mostly based on content-based recommendation algorithms [9; 10], i.e., recommendations using the degree of similarity in content. The user's interests are modeled by analyzing the features and attributes of the items. The user's preferences are evaluated by calculating the similarity between their preferred items and other items. Although content-based recommendation algorithms have good explanatory power, they may suffer from \"information filtering\", i.e., if the user's history is too small or too homogeneous, the recommendation results may not be diverse enough. Most subsequent work has focused on traditional collaborative filtering-based recommendation algorithms [11; 12; 13; 14; 15; 16; 17; 18], which do not require prior feature extraction or modeling of items. Collaborative filtering-based recommendation algorithms are mainly divided into two types of algorithms: user-based [19; 20], which recommends products of interest to similar users, and item-based [21; 22], which recommends items to users of similar items. However, such algorithms generally suffer from user-item interaction data sparsity [23; 24] (i.e., unsatisfactory recommendations when user history interaction data are sparse) and cold starts [25; 26] (i.e., it is difficult to recommend products to new users while also recommending brand new items that have not been explored in depth). To alleviate the shortcomings of the above algorithm problem, other information sources can be introduced, such as users' personal information [27], user comments [28; 29], item attribute information [30] or knowledge graphs [31; 32; 33; 34; 35; 36; 37; 38]. There is a large amount of auxiliary information that can be used by the recommendation algorithm, and the recommendation system can filter or sort the recommendation results based on this auxiliary information, so as to improve the diversity, real-time nature and accuracy of recommendations and meet  
Users, items and their attributes can be mapped to a knowledge graph in a recommendation task, which can be used to further explore the potential relationship between users and items [33]. Feature learning through knowledge graphs can represent low-dimensional features of users and items [34], be useful for mining potential information at a deeper level of item granularity [36], and distinguish different levels of importance among items in a user's history [35], etc. [PERSON] et al. proposed a Power Fault Retrieval and Recommendation Model (PF2 RM) [39], where they use the graph-neighbor cluster polymerization method to update the cold-start prediction sequence for the power knowledge graph. [PERSON] et al. proposed RippleNet [33], an end-to-end click-through prediction model using knowledge graphs as a source of auxiliary information, to continuously explore the potential interests of users by simulating the process of propagation of their interests.