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Kyungho Kim

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Book Open access Aug 2026

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

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/.

Sunwoo Kim, Geon Lee, Kyungho Kim et al. · Proceedings of the 32nd ACM... · 0 citations

Preprint Aug 2026

Training-Free LLM-Based Recommendation with Post-LLM Item Refinement Using Collaborative Signals

Large language models (LLMs) have shown promise for training-free recommendation, but LLM-generated user interests are often too broad for fine-grained item retrieval. Existing methods incorporate collaborative filtering (CF) signals in a pre-LLM manner through candidate reranking or prompt augmentation, yielding limited gains. We propose CoRRe, a training-free recommendation framework with a post-LLM paradigm that injects CF signals into LLM-generated item representations, which are later matched with LLM-generated user interests for ranking. Specifically, CoRRe refines the directions of item embeddings using an item-item co-purchase graph and their magnitudes using item popularity. Experiments on real-world datasets show that CoRRe consistently outperforms existing training-free methods and achieves competitive or superior performance compared with training-based methods, without requiring any model training or task-specific fine-tuning.

Kyungho Kim, Sunwoo Kim, Geon Lee et al. · 0 citations