Agentic systems powered by Large Language Models (LLMs) have shown strong potential in recommender systems but remain hindered by several challenges. Fine-tuning LLMs is parameter-inefficient, and prompt-based agentic reasoning is limited by context length and hallucination risk. Moreover, existing agentic recommendation systems predominantly leverages semantic knowledge while neglecting the collaborative filtering (CF) signals essential for implicit preference modeling. To address these limitations, we propose AMEM4Rec, an agentic LLM-based recommender that learns collaborative signals in an end-to-end manner through cross-user memory evolution. AMEM4Rec stores abstract user behavior patterns from user histories in a global memory pool. Within this pool, memories are linked to similar existing ones and iteratively evolved to reinforce shared cross-user patterns, enabling the system to become aware of CF signals without relying on a pre-trained CF model. Extensive experiments on Amazon and MIND datasets show that AMEM4Rec consistently outperforms state-of-the-art LLM-based recommenders, demonstrating the effectiveness of evolving memory-guided collaborative filtering.

翻译：基于大型语言模型（LLM）的智能系统在推荐系统中展现出巨大潜力，但仍面临若干挑战。微调LLM存在参数效率低下的问题，而基于提示的智能推理则受限于上下文长度和幻觉风险。此外，现有的智能推荐系统主要依赖语义知识，却忽略了隐式偏好建模所必需的协同过滤信号。为克服这些局限，我们提出AMEM4Rec——一种基于智能LLM的推荐系统，通过跨用户记忆演化以端到端方式学习协同信号。AMEM4Rec将用户历史中的抽象行为模式存储于全局记忆池中。在该记忆池内，新记忆会与相似的既有记忆建立关联，并通过迭代演化强化跨用户共享模式，使系统无需依赖预训练的协同过滤模型即可感知协同信号。在Amazon和MIND数据集上的大量实验表明，AMEM4Rec持续优于当前最先进的基于LLM的推荐系统，验证了记忆引导的协同过滤演化机制的有效性。