# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 1 Introduction
Well-known knowledge graphs include Google Knowledge Graph, Wikidata, DBpedia, ConceptNet, FreeBase, and YAGO. The combination of knowledge graphs and recommendation systems has provided a strong impetus for the rapid development of intelligent tutoring systems, given the powerful capabilities of knowledge graphs in knowledge representation [4]. The application of knowledge graphs in the field of recommendation systems has become increasingly widespread and deep. User needs can be better understood through structured knowledge representation that includes rich contextual information and multi-dimensional features, thereby enhancing the accuracy and relevance of recommendations. Various types of auxiliary information are utilized to better identify users' latent interests and item characteristics to improve the accuracy of recommendation algorithms. In recent years, recommendation systems based on knowledge graphs have garnered widespread attention from both academia and industry by fully leveraging the hidden information in graph-structured data, achieving a series of research results that not only improve recommmendation accuracy but also enhance the interpretability of recommendation results. To fully exploit the auxiliary information in knowledge graphs, existing research primarily adopts propagation-based methods to integrate representations of entities and relationships as well as higher-order connection patterns to achieve more personalized recommendations. A common implementation of this approach is based on graph neural network technology. [PERSON] et al. [5] proposed the RippleNet model, which introduced a preference propagation mechanism in knowledge graphs for the first time. It automatically and iteratively extends users' latent preferences along the connections in the knowledge graph, thereby propagating user preferences across the set of knowledge entities. [PERSON] et al. [6] proposed the Knowledge Graph Attention Network (KGAT) model, which explicitly models higher-order connections in knowledge graphs in an end-to-end manner. The KGAT model recursively propagates information from a node's neighbors (including users, items, or attributes) to refine the node's embedding representation and includes an attention mechanism to differentiate the importance of neighbors. [PERSON] et al. [7] proposed the Collaborative Knowledge-Aware Attentive Network (CKAN), which explicitly encodes collaborative signals through collaborative propagation and naturally integrates them with knowledge associations. The CKAN effectively fuses collaborative information with knowledge information, demonstrating significant superiority. Inspired by the effectiveness of contrastive learning in extracting supervisory signals from data, [PERSON] et al. [8] focused on exploring contrastive learning in knowledge-aware recommendation (KGR) and proposed a multi-level, interactive contrastive learning mechanism. Unlike traditional contrastive learning methods that contrast nodes in two generated graph views, KGIC's interactive contrastive mechanism performs layer-by-layer self-supervised learning by contrasting layers within the graph. Subsequently, a large number of propagation-based iterative update models have been proposed, such as KGRec [9] and EditKG [10]. [PERSON] et al. [11] proposed a new framework called BoxGNN, which performs message aggregation through combinations of logical operations to merge higher-order signals. The integration of knowledge graphs and recommendation systems in the field of education is a highly promising direction. The recommendation system based on knowledge graphs has achieved certain successes in the field of learning resource recommendation, making progress in areas such as courses, exercises, academic papers, books, and learning peers. [PERSON] et al.