A Tutorial on Retrieval-Augmented Generation for LLM-based Recommender Systems

Abstract

Recommender systems are essential to real-world online services due to their substantial business value. Increasingly, large language models (LLMs) have been adopted for recommendation tasks, owing to their strong reasoning capabilities and their effectiveness in handling cold-start items. A large body of work has sought to adapt retrieval-augmented generation (RAG) for LLM-based recommender systems by retrieving information from external databases and integrating it into the LLM's input to improve recommendation quality. Despite the rapid growth of the field, existing tutorials lack a clear taxonomy and a dedicated review of RAG methods for LLM-based recommender systems. To fill this gap, we present the first tutorial on this topic. First, we discuss the types of information used in LLM-based recommender systems, including target-user information and external databases for retrieval. Second, we present the design space of RAG for LLM-based recommender systems, outlining the key design choices at each stage of the pipeline: (1) retrieval, (2) augmentation, and (3) generation. Lastly, we introduce open questions for future research on this topic. Tutorial materials, including slides, are available at https://recsys-rag-tutorial.github.io/.