CLLMRec: 基于大语言模型的认知感知概念推荐：通过语义对齐和先决知识蒸馏
CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillation
AI总结：
CLLMRec通过语义对齐和先决知识蒸馏，利用大语言模型实现认知感知的概念推荐，无需依赖结构化知识图谱。
摘要：
大规模开放在线课程（MOOCs）的兴起给个性化学习带来了重大挑战，其中概念推荐至关重要。现有方法通常依赖于异构信息网络或知识图谱来捕捉概念关系，并结合知识追踪模型来评估学习者的认知状态。然而，这些方法由于依赖高质量的结构化知识图谱，而在现实世界教育场景中往往稀缺。为了解决这一根本挑战，本文提出CLLMRec，一种新的框架，通过两个协同的技术支柱：语义对齐和先决知识蒸馏来利用大语言模型。语义对齐组件通过编码学习者和概念的非结构化文本描述构建统一的表示空间。先决知识蒸馏范式采用教师-学生架构，其中大型教师LLM（实现为先验知识感知组件）从其内部化的世界知识中提取概念先决关系，并将其蒸馏为软标签以训练高效的student排名器。基于这些基础，我们的框架集成了一个精细排名机制，通过深度知识追踪显式建模学习者的实时认知状态，确保推荐在结构上稳固且认知上合适。在两个真实世界MOOC数据集上的广泛实验表明，CLLMRec在多个评估指标上显著优于现有基线方法，验证了其在生成真正认知感知和个性化概念推荐方面的有效性，而无需依赖显式的结构先验。
英文摘要：
The growth of Massive Open Online Courses (MOOCs) presents significant challenges for personalized learning, where concept recommendation is crucial. Existing approaches typically rely on heterogeneous information networks or knowledge graphs to capture conceptual relationships, combined with knowledge tracing models to assess learners' cognitive states. However, these methods face significant limitations due to their dependence on high-quality structured knowledge graphs, which are often scarce in real-world educational scenarios. To address this fundamental challenge, this paper proposes CLLMRec, a novel framework that leverages Large Language Models through two synergistic technical pillars: Semantic Alignment and Prerequisite Knowledge Distillation. The Semantic Alignment component constructs a unified representation space by encoding unstructured textual descriptions of learners and concepts. The Prerequisite Knowledge Distillation paradigm employs a teacher-student architecture, where a large teacher LLM (implemented as the Prior Knowledge Aware Component) extracts conceptual prerequisite relationships from its internalized world knowledge and distills them into soft labels to train an efficient student ranker. Building upon these foundations, our framework incorporates a fine-ranking mechanism that explicitly models learners' real-time cognitive states through deep knowledge tracing, ensuring recommendations are both structurally sound and cognitively appropriate. Extensive experiments on two real-world MOOC datasets demonstrate that CLLMRec significantly outperforms existing baseline methods across multiple evaluation metrics, validating its effectiveness in generating truly cognitive-aware and personalized concept recommendations without relying on explicit structural priors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
