FIRM: Fine-grained Item Relational Memory for LLM-based Recommender Agents

LLM-based recommender agents store user preferences and item characteristics in natural-language memory, and reason over item text and interaction history to rerank candidate items. Recent work improves this reranking with two predefined item relation types, substitute and complement, which reveal user intent implicit in interaction data. However, these types cannot capture the specific comparison criterion: regular vs. decaffeinated beans and small vs. bulk packs are both substitutes, yet differ in caffeine content and package size, respectively. Moreover, the relations are re-inferred for every user rather than stored as knowledge shared across users. Simply generating fine-grained relations produces redundant types with inconsistent definitions. In this paper, we propose Fine-grained Item Relational Memory (FIRM), a shared memory that organizes fine-grained item relations into a consistent type hierarchy. FIRM is built by three agents: the Explorer extracts a fine-grained relation for each item pair; the Manager organizes the types into a relation tree by merging redundant ones and splitting those that mix distinct criteria; and the Verifier checks type definitions for consistency. During inference, the relations among the user's history items are summarized into a user memory of behavioral tendencies. The recommender agent then retrieves the relations between history items and candidates from FIRM and reranks the candidates based on how well these relations match the user's tendencies. On three real-world datasets, FIRM improves NDCG@10 over the strongest baseline by up to 10.03%, and a memory built once remains effective across different LLMs.