ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation

ArXi:2511.15141v2 Announce Type: replace-cross Recently, large language models (LLMs) have been widely used as recommender systems, owing to their reasoning capability and effectiveness in handling cold-start items. A common approach prompts an LLM with a target user's purchase history to recommend items from a candidate set, often enhanced with retrieval-augmented generation (RAG