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). Most existing RAG approaches retrieve purchase histories of users similar to the target user; however, these histories often contain noisy or weakly relevant information and provide little or no useful information for candidate items. To address these limitations, we propose ItemRAG, a novel RAG approach that shifts focus from coarse user-history retrieval to fine-grained item-level retrieval. ItemRAG augments the description of each item in the target user's history or the candidate set by retrieving items relevant to each. To retrieve items not merely semantically similar but informative for recommendation, ItemRAG leverages co-purchase information alongside semantic information. Especially, through their careful combination, ItemRAG prioritizes more informative retrievals and also benefits cold-start items. Through extensive experiments, we demonstrate that ItemRAG consistently outperforms existing RAG approaches under both standard and cold-start item recommendation settings. Supplementary materials, code, and datasets are provided at https://github.com/kswoo97/ItemRAG.

翻译：最近，大语言模型（LLMs）因其推理能力和处理冷启动物品的高效性，被广泛用作推荐系统。一种常见方法是通过目标用户的购买历史提示LLM从候选集中推荐物品，通常结合检索增强生成（RAG）进行优化。现有的大多数RAG方法会检索与目标用户相似用户的购买历史；然而，这些历史往往包含噪声或弱相关信息，且对候选物品几乎不提供有用信息。为解决这些局限性，我们提出ItemRAG——一种新型RAG方法，将焦点从粗粒度的用户历史检索转向细粒度的物品级检索。ItemRAG通过检索与每个物品相关的历史或候选集中物品的描述信息，增强其描述效果。为了检索既非仅语义相似、又能为推荐提供信息的物品，ItemRAG利用共同购买信息与语义信息相结合。特别是，通过精心组合这些信息，ItemRAG优先选择信息量更大的检索结果，同时惠及冷启动物品。通过大量实验，我们证明ItemRAG在标准推荐和冷启动物品推荐设置下均持续优于现有RAG方法。补充材料、代码和数据集可在https://github.com/kswoo97/ItemRAG获取。