RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation

TL;DR

RRCM is a flexible, ranking-driven framework that improves LLM-based recommendations by adaptively retrieving and reasoning over collaborative and metadata memories, leading to better recommendation quality.

Contribution

The paper introduces RRCM, a novel retrieval-and-reasoning framework that dynamically selects evidence sources for LLM recommenders, overcoming fixed context strategies and efficiency bottlenecks.

Findings

RRCM significantly outperforms traditional baselines.

RRCM achieves higher recommendation accuracy.

Flexible evidence retrieval improves recommendation quality.

Abstract

Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and natural-language reasoning abilities. Despite recent progress, current LLM-based recommenders still face key challenges in constructing decision-relevant contexts from heterogeneous evidence. First, existing methods often rely on fixed context construction strategies: collaborative behavioral evidence and item-side metadata are typically incorporated through predefined prompts, static retrieval pipelines, or handcrafted injection mechanisms, making it difficult to determine what information is truly beneficial for each instance. Second, heterogeneous evidence introduces a severe context-efficiency bottleneck. Rich metadata and collaborative interaction records can quickly overwhelm the context window, while aggressive compression or heuristic filtering may discard fine-grained evidence critical for accurate recommendation. To address these challenges, we propose RRCM, a ranking-driven retrieval-and-reasoning framework over collaborative and metadata memories for LLM-based agentic recommendation. RRCM starts from a lightweight user-history context and learns whether to recommend directly, retrieve collaborative evidence, retrieve item metadata, or interleave both through reasoning. Both memories are represented in natural language and accessed through a unified retrieval interface, enabling flexible evidence acquisition without handcrafted CF injection or fixed retrieval rules. We optimize this memory-reading policy with an outcome-only ranking reward, instantiated using group relative policy optimization, so that retrieval decisions are directly driven by final top-k recommendation quality. Extensive experiments show that RRCM significantly outperforms traditional baselines and diverse LLM-based recommendation approaches.