Abstract: Personalized learning recommendation is an important research topic in the field of intelligent education, and its core goal is to use recommendation algorithms and models to provide learners with effective learning resources that match their individual learning needs, interests, abilities and learning histories, so as to improve learners' learning effectiveness. Current recommendation methods have problems such as cold start, data scarcity, poor interpretability, and over-personalization, etc. The combination of knowledge graph and large language model provides strong support to solve the above problems. First, from the field of personalized learning recommendation, we provide an overview of its concept and current research status. Second, the concepts of knowledge graph and big language model and their specific applications in personalized learning recommendation are discussed respectively. Once again, summarize the methods of collaborative application of knowledge graph and large language model in personalized learning recommendation. Finally, it looks forward to the future development direction of knowledge graph and large language model in personalized learning recommendation to provide reference and inspiration for the continuous development and innovative practice in the field of personalized learning recommendation.

Key words: knowledge graph, large language model, personalized learning, recommendation algorithm, learning resources

摘要： 个性化学习推荐是智能教育领域的重要研究课题，其核心目标是利用推荐算法和模型为学习者提供与其个人学习需求、兴趣、能力和学习历史相匹配的有效学习资源，从而提高学习者的学习效果。目前推荐方法存在冷启动、数据稀缺、可解释性差、过度个性化等问题，知识图谱与大语言模型的结合为解决上述问题提供了有力支持。首先，从个性化学习推荐领域出发，对其概念、研究现状等内容进行概述。其次，分别讨论知识图谱和大语言模型的概念以及在个性化学习推荐中的具体应用。再次，总结知识图谱与大语言模型在个性化学习推荐中协同应用的方法。最后，展望知识图谱和大语言模型在个性化学习推荐中未来发展方向，为个性化学习推荐领域的持续发展和创新实践提供借鉴和启示。

关键词: 知识图谱, 大语言模型, 个性化学习推荐, 推荐算法, 学习资源