# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 1 Introduction  
An intelligent tutoring system (ITS) is an educational tool that leverages artificial intelligence and big data technologies to provide personalized learning support and services to learners [1]. With the rapid development of AI technologies, educational intelligent agents such as ITSs are gaining increasing attention in the field of education, as they can offer tailored learning paths and personalized learning resources based on learners' interests, preferences, and characteristics. An ITS represents a significant area of mutual empowerment between AI and education, serving as an effective means to promote large-scale personalized learning and achieve precise workload reduction and efficiency enhancement, thereby facilitating the digital transformation of education. Research focusing on the threekey elements of the learning process, scenarios, subjects, and services, should be conducted on the unobtrusive perception and intelligent recognition of learning scenarios, the cognitive tracking and attribution inference of learning subjects, and the dynamic and precise adaptation of learning services. This will lead to the formation of a technical system where \"learning scenarios are computable, learning subjects are comprehensible, and learning services are adaptable\", thereby constructing an intelligent tutoring system [2]. A recommendation system is an information filtering system that utilizes algorithms and data to provide users with personalized content, products, or services [3]. As an integral part of modern information technology, recommendation systems deeply analyze users' historical behavior data and item characteristics to offer suggestions that align with their interests and preferences. Recommendation systems are mainly classified into content-based recommendation, collaborative filtering recommendation, and hybrid recommendation. They are widely applied in e-commerce, social media, content platforms, education, and other fields to enhance user experience and increase user engagement. The latest recommendation systems leverage advanced technologies such as big data and artificial intelligence, significantly promoting the development of recommendation systems in both academia and industry. Education is a key development area for recommendation systems providing intelligent and personalized services for learning path planning, personalized learning, high-quality learning resource recommendation, social learning, etc. These services can effectively improve learners' learning experiences and educational quality, making them an important component of intelligent tutoring systems and a significant research direction for educational intelligent agents as well as a field worthy of in-depth study. A knowledge graph represents knowledge in a graph format and is used to describe entities and their relationships with each other. It is an important research direction in the field of artificial intelligence. A knowledge graph typically consists of entities and relationships, enabling the efficient representation of complex interconnected information. The technical framework for constructing a knowledge graph can be divided into three levels: the information extraction layer, the knowledge integration layer, and the knowledge processing layer. The information extraction layer is responsible for extracting entities and relationships from multiple data sources; the knowledge integration layer processes knowledge from different sources, resolving data conflicts and redundancy issues; and the knowledge processing layer further processes and optimizes the extracted and integrated knowledge. A knowledge graph is an intelligent and efficient means of organizing knowledge, which not only helps users quickly and accurately obtain the information they need but also plays a significant role in multiple fields, including search engines, recommendation systems, question-answering systems, natural language processing, and intelligent assistants.