AtomRec：面向智能体推荐的演化原子记忆

中文摘要 AI 辅助

智能体推荐系统利用大语言模型维护语义记忆，支持基于证据的推荐。但现有记忆机制常将用户与物品信息压缩为粗粒度摘要，并通过标量协同链接关联，难以在用户兴趣演化时保留细粒度偏好阶段或检索可解释证据。本文提出AtomRec，一种具备演化原子协同记忆的智能体推荐系统，将用户与物品记忆表示为结构化原子单元，在相关记忆间建立语义链接，当新交互到达时演化相关历史字段。推荐时，该系统将关联记忆作为多跳证据路径而非孤立邻居摘要检索，使协同信号支持可解释排序。在四个公开基准上的实验显示，AtomRec在指标上较现有最优智能体及记忆增强基线始终表现更优，平均相对提升约8.5%。

英文摘要

Agentic recommender systems use large language models to maintain semantic memory and support evidence-aware recommendation. However, existing memory mechanisms often compress user and item information into coarse summaries and connect them with scalar collaborative links, making it difficult to preserve fine-grained preference stages or retrieve interpretable evidence as user interests evolve. We propose \textsc{AtomRec}, an agentic recommender with evolving atomic collaborative memory. \textsc{AtomRec} represents user and item memories as structured atomic units, builds semantic links across related memories, and evolves related historical fields when new interactions arrive. During recommendation, it retrieves linked memories as multi-hop evidence paths rather than isolated neighbor summaries, allowing collaborative signals to support grounded ranking. Experiments on four public benchmarks show that \textsc{AtomRec} consistently outperforms state-of-the-art agentic and memory-augmented baselines, with around 8.5\% average relative improvement across metrics.

发表机构

• Xi’an Jiaotong-Liverpool University（西交利物浦大学）
• Xiaohongshu（小红书）
• Peking University（北京大学）
• East China Normal University（华东师范大学）
• Beijing Jiaotong University（北京交通大学）

机构由 AI 辅助整理，请以论文原文为准。