用智能体架构自动完成推荐系统全周期迭代，减少人工干预。
AgenticRS-Architecture: System Design for Agentic Recommender Systems
- 用三个智能体分别管理模型、特征和资源的演化与优化
- 实现从方法解析到代码生成、训练、评估的闭环自动化
- 适合需要持续优化的工业级推荐系统团队使用
AutoModel 是一种基于智能体的工业推荐系统全生命周期架构。不同于固定召回与排序流程，该架构将推荐任务组织为具备长期记忆与自我改进能力的相互协作智能体。我们实例化了沿模型、特征、资源维度的三个核心智能体：AutoTrain 负责模型设计与训练，AutoFeature 负责数据分析与特征演化，AutoPerf 负责性能优化、部署与在线实验。一个共享的协调与知识层连接各智能体，并记录决策、配置与结果。通过名为 paper autotrain 的模块案例研究，展示了 AutoTrain 如何实现论文驱动的模型复现——从方法解析、代码生成、大规模训练到离线对比的闭环，显著降低方法迁移的人工成本。AutoModel 实现了大规模推荐系统的本地自动化、全局协同演化，可推广至搜索与广告等其他 AI 系统。
原文摘要 · Abstract (English)
AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendation as a set of interacting evolution agents with long term memory and self improvement capability. We instantiate three core agents along the axes of models, features, and resources: AutoTrain for model design and training, AutoFeature for data analysis and feature evolution, and AutoPerf for performance, deployment, and online experimentation. A shared coordination and knowledge layer connects these agents and records decisions, configurations, and outcomes. Through a case study of a module called paper autotrain, we show how AutoTrain automates paper driven model reproduction by closing the loop from method parsing to code generation, large scale training, and offline comparison, reducing manual effort for method transfer. AutoModel enables locally automated yet globally aligned evolution of large scale recommender systems and can be generalized to other AI systems such as search and advertising.
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