让推荐系统自动进化，边优化边积累方法论。
EvoRec: Self Evolving Agentic Recommender Systems
- 四代理协同，模型与优化方法同步演化
- 离线指标最高提升5.54%，线上点击率增1.02%
- 适合想自动化推荐系统迭代的工程师和研究者
现代推荐系统的优化仍严重依赖人工迭代，效率低且受限于个人能力。基于大语言模型的智能体虽能加速这一过程，但存在两大问题：一是仅作为代码翻译工具，无法积累优化方法；二是优化空间局限于预设范围，难产生结构创新。为此，我们提出EvoRec，一种多智能体框架，实现推荐模型与优化方法的共同进化。四个协作智能体执行双轨循环：研究智能体与代码智能体每轮迭代模型，技能演进智能体则定期从持续记忆中提炼可复用的方法论。在两个公开基准和一个大规模工业数据集上的实验表明，EvoRec相比最强基线离线指标最高提升5.54%；在线A/B测试中带来1.85%收入增长和1.02%点击率提升。
原文摘要 · Abstract (English)
Optimizing modern recommender systems still relies heavily on engineers iterating by hand, which is slow and bounded by individual expertise. LLM-based agents open a path toward automating this loop, yet two issues remain. First, the agent is used only as a code translator and accumulates no methodology across iterations. Second, the optimization space is confined to a predefined range and rarely introduces structurally new ideas. To address these problems, we propose EvoRec, a multi-agent framework that co-evolves the recommendation model and the optimization methodology driving it. Four collaborating agents carry out a dual-track loop: the Research Agent and Code Agent iterate the model each round, while the Skill Evolver periodically distills reusable methodology from a persistent Memory of past experiments. Experiments on two public benchmarks and one large-scale industrial dataset show that EvoRec improves offline metrics by up to 5.54% over the strongest baseline, and an online A/B test delivers a 1.85% revenue lift and a 1.02% CTR gain.
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