# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 2 Related Work
### Personalized Learning Services for Intelligent Education
The research results show that when learning content and strategies match students' cognition and learning styles, their comprehension, memory, and academic performance are effectively improved. [PERSON] et al. [27] discussed how to use adaptive learning technology to provide students with a tailored learning experience and enhance learning outcomes and academic performance. They pointed out that artificial intelligence (AI) and machine learning (ML) algorithms can dynamically adjust the difficulty and depth of learning content based on students' knowledge mastery and learning progress, creating personalized learning paths for students while incorporating a real-time feedback mechanism. [PERSON] et al. [28] proposed an AI-based intelligent assistant system (AIIA) aimed at supporting personalized and adaptive learning in higher education. This system deeply integrates with learning management systems (LMSs) using technologies such as natural language processing (NLP) and large language models. Its personalized learning services include dynamic learning resource generation, adaptive learning paths, immediate real-time feedback and assessment, and emotional support, thereby achieving a highly personalized learning experience. Regarding learning resource recommendation via knowledge graphs, [PERSON] et al. [29] proposed a new method for recommending learning resources through a knowledge graph and learning-style clustering. In this method, a knowledge graph is constructed based on a general ontology model, and graph embedding algorithms and graph matching processes are applied to optimize the efficiency of graph computation in order to identify similar learning resources. [PERSON] et al. [30] introduced a learning resource recommendation model based on a Collaborative Knowledge Graph Attention Network (CKALR). This model constructs knowledge graphs using the attributes of teaching resources, naturally integrating the explicit collaborative signals between learners and learning resources with the auxiliary knowledge in the graph. An attention mechanism is employed to accurately capture the implicit personal preferences inherent in learners' past interaction information, further enriching the feature representation of both learners and learning resources. [PERSON] et al. [31] employed a recommendation algorithm that combines knowledge graphs and collaborative filtering. They collected and modeled implicit data from online courses and compared the impact of video and text learning resources on user learning needs with different weights in order to gain deeper insights into the differing contributions of video and text learning resources in meeting learning needs. [PERSON] et al. [32] proposed the MkEGC framework for learning resource recommendation, treating the recommendation process as a Markov decision process. They constructed a dual-knowledge graph convolutional network that operates in both the learning resource knowledge and learner knowledge domains. Hierarchical and attention weighting strategies were designed to effectively extract latent hierarchical information from the knowledge graphs. Despite the progress made since the introduction of knowledge-graph-based recommendation systems in personalized learning, two pressing issues remain in the realm of personalized learning resource recommendation: (1) Much of the research has focused on the representation of learning resources while neglecting the representation of learners. (2) The strategies for aggregating multi-source heterogeneous information tend to be overly simplistic and can result in information loss and the failure to differentiate the significance of various neighboring entities. Our work differs from existing studies by proposing a novel, end-to-end, personalized learning resource recommendation model called LKGA. This model simultaneously attends to user collaborative signals and learning resource collaborative signals, providing a more comprehensive representation of the original entity sets of learners and learning resources.