用用户行为记忆演化实现无需预训练模型的协同过滤。
AMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders
- 通过跨用户记忆关联与迭代演化，自动学习协同信号。
- 在Amazon和MIND数据集上超越现有LLM推荐系统性能。
- 适合关注个性化推荐与记忆增强机制的研究者。
基于大语言模型（LLMs）的智能体系统在推荐系统中展现出巨大潜力，但仍面临参数效率低、上下文长度受限及幻觉风险等问题。现有系统多依赖语义知识，忽视协同过滤（CF）对隐式偏好建模的关键作用。为此，我们提出AMEM4Rec，一种端到端学习协同信号的智能体推荐框架。该方法将用户历史中的抽象行为模式存入全局记忆池，通过与相似记忆关联并迭代演化，强化跨用户共享模式，从而无需预训练CF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，验证了记忆驱动协同过滤的有效性。
原文摘要 · Abstract (English)
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.
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