Massive Open Online Courses (MOOCs) have significantly enhanced educational accessibility by offering a wide variety of courses and breaking down traditional barriers related to geography, finance, and time. However, students often face difficulties navigating the vast selection of courses, especially when exploring new fields of study. Driven by this challenge, researchers have been exploring course recommender systems to offer tailored guidance that aligns with individual learning preferences and career aspirations. These systems face particular challenges in effectively addressing the ``cold start'' problem for new users. Recent advancements in recommender systems suggest integrating large language models (LLMs) into the recommendation process to enhance personalized recommendations and address the ``cold start'' problem. Motivated by these advancements, our study introduces RAMO (Retrieval-Augmented Generation for MOOCs), a system specifically designed to overcome the ``cold start'' challenges of traditional course recommender systems. The RAMO system leverages the capabilities of LLMs, along with Retrieval-Augmented Generation (RAG)-facilitated contextual understanding, to provide course recommendations through a conversational interface, aiming to enhance the e-learning experience.

翻译：大规模开放在线课程（MOOCs）通过提供多样化的课程选择，有效打破了传统教育在地理、经济和时间方面的障碍，显著提升了教育的可及性。然而，面对海量的课程资源，学生往往难以进行有效筛选，尤其是在探索全新学科领域时。针对这一挑战，研究者们致力于开发课程推荐系统，以提供符合个人学习偏好与职业发展目标的个性化指导。这些系统在应对新用户的“冷启动”问题上尤其面临困难。推荐系统领域的最新进展表明，将大语言模型（LLMs）整合到推荐流程中，可以提升个性化推荐效果并缓解“冷启动”问题。受此启发，本研究提出了RAMO（面向MOOCs的检索增强生成系统），该系统专门设计用于克服传统课程推荐系统中的“冷启动”挑战。RAMO系统充分利用大语言模型的能力，并结合检索增强生成（RAG）技术所促进的上下文理解，通过对话式交互界面提供课程推荐，旨在提升在线学习体验。