Abstract:

As an important research topic in the field of smart education， personalized learning recommendation has a core goal of using recommendation algorithms and models to provide learners with effective learning resources that match their individual learning needs， interests， abilities， and histories， so as to improve learners’ learning effects. Current recommendation methods have problems such as cold start， data sparsity， poor interpretability， and over-personalization， and the combination of knowledge graph and Large Language Model （LLM） provides strong support to solve the above problems. Firstly， the contents such as concepts and current research status of personalized learning recommendation were overviewed. Secondly， the concepts of knowledge graph and LLM and their specific applications in personalized learning recommendation were discussed respectively. Thirdly， the collaborative application methods of knowledge graph and LLM in personalized learning recommendation were summarized. Finally， the future development directions of knowledge graph and LLM in personalized learning recommendation were prospected to provide reference and inspiration for continuous development and innovative practice in the field of personalized learning recommendation.

Key words: knowledge graph, Large Language Model (LLM), personalized learning recommendation, recommendation algorithm, learning resource

摘要：

个性化学习推荐是智慧教育领域的重要研究课题，它的核心目标是利用推荐算法和模型为学习者提供与他们的个人学习需求、兴趣、能力和历史相匹配的有效学习资源，从而提高学习者的学习效果。目前的推荐方法存在冷启动、数据稀疏、可解释性差和过度个性化等问题，而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，对个性化学习推荐的概念、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（LLM）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LLM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LLM在个性化学习推荐中的未来发展方向，从而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