mlsanityData to 7 Oct 2026

RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations

arXiv 2407.04925 ↗First posted 6 Jul 2024cs.IR cs.AI cs.HCcited by 6

On the map: Retrieval-augmented generation → RAG for recommendation

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation. Builds on 10 of its 34 references that are in the map. First posted on arXiv 6 Jul 2024.

Builds on

10 of 34 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2024Large Language Models for Education: A Survey and Outlookcited by 185
  4. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preferencecited by 37
  5. 2023Retrieval-Generation Synergy Augmented Large Language Modelscited by 26
  6. 2023Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendationscited by 15
  7. 2024How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responsescited by 12
  8. 2024Improving Assessment of Tutoring Practices using Retrieval-Augmented Generationcited by 11
  9. 20243DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systemscited by 9
  10. 2024Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)cited by 6
  2. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 3
  3. 2025Automatic Large Language Models Creation of Interactive Learning Lessonscited by 2
  4. 2025ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systemscited by 2
  5. 2025POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applicationscited by 1
  6. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries
  2. 2025Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs
  3. 2025TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
  4. 2024RAGSys: Item-Cold-Start Recommender as RAG System
  5. 2024Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
  6. 2025WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
  7. 2026RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender
  8. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation