RRCM：面向 LLM 推荐的排序驱动协同记忆与元记忆检索

Computer Science > Information Retrieval arXiv:2605.07129 (cs) [Submitted on 8 May 2026 (v1), last revised 27 Sep 2026 (this version, v2)] Title:RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation Authors:Shijun Li, Pranav Belligundu, Tianxin Wei, Wooseong Yang, Yu Wang, Joydeep Ghosh View a PDF of the paper titled RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation, by Shijun Li and 5 other authors View PDF HTML (experimental) 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. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2605.07129 [cs.IR] (or arXiv:2605.07129v2 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2605.07129 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shijun Li [view email] [v1] Fri, 8 May 2026 02:07:30 UTC (2,103 KB) [v2] Sun, 27 Sep 2026 19:16:36 UTC (2,119 KB) Full-text links: Access Paper: View a PDF of the paper titled RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation, by Shijun Li and 5 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR new | recent | 2026-05 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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