The evolution of recommender systems has shifted preference storage from rating matrices and dense embeddings to semantic memory in the agentic era. Yet existing agents rely on isolated memory, overlooking crucial collaborative signals. Bridging this gap is hindered by the dual challenges of distilling vast graph contexts without overwhelming reasoning agents with cognitive load, and evolving the collaborative memory efficiently without incurring prohibitive computational costs. To address this, we propose MemRec, a framework that architecturally decouples reasoning from memory management to enable efficient collaborative augmentation. MemRec introduces a dedicated, cost-effective LM_Mem to manage a dynamic collaborative memory graph, serving synthesized, high-signal context to a downstream LLM_Rec. The framework operates via a practical pipeline featuring efficient retrieval and cost-effective asynchronous graph propagation that evolves memory in the background. Extensive experiments on four benchmarks demonstrate that MemRec achieves state-of-the-art performance. Furthermore, architectural analysis confirms its flexibility, establishing a new Pareto frontier that balances reasoning quality, cost, and privacy through support for diverse deployments, including local open-source models. Code:https://github.com/rutgerswiselab/memrec and Homepage: https://memrec.weixinchen.com

翻译：推荐系统的演进已从评分矩阵和稠密嵌入转向智能体时代的语义记忆存储。然而现有智能体依赖孤立记忆，忽视了关键的协作信号。填补这一空白面临双重挑战：既要从庞大图上下文中提炼信息，避免推理智能体认知过载；又要高效更新协作记忆，避免产生过高计算成本。为此，我们提出MemRec框架，通过架构设计将推理与记忆管理解耦，实现高效的协作增强。MemRec引入专用的轻量化LM_Mem模块来管理动态协作记忆图，为下游LLM_Rec提供合成的高信号上下文。该框架通过实用化流程运行，包含高效检索机制和轻量级异步图传播技术，可在后台持续更新记忆。在四个基准数据集上的大量实验表明，MemRec实现了最先进的性能。架构分析进一步证实其灵活性：通过支持包括本地开源模型在内的多样化部署方案，MemRec建立了平衡推理质量、计算成本与隐私保护的新帕累托前沿。代码：https://github.com/rutgerswiselab/memrec 主页：https://memrec.weixinchen.com