Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed search space defined by human priors, limiting innovation to pre-defined operators. While recent LLM-driven code evolution frameworks shift fixed search space target to open-ended program spaces, they primarily rely on scalar metrics (e.g., NDCG, Hit Ratio) that fail to provide qualitative insights into model failures or directional guidance for improvement. To address this, we propose Self-EvolveRec, a novel framework that establishes a directional feedback loop by integrating a User Simulator for qualitative critiques and a Model Diagnosis Tool for quantitative internal verification. Furthermore, we introduce a Diagnosis Tool - Model Co-Evolution strategy to ensure that evaluation criteria dynamically adapt as the recommendation architecture evolves. Extensive experiments demonstrate that Self-EvolveRec significantly outperforms state-of-the-art NAS and LLM-driven code evolution baselines in both recommendation performance and user satisfaction. Our code is available at https://github.com/Sein-Kim/self_evolverec.

翻译：传统自动化推荐系统设计方法（如神经架构搜索）通常受限于由人类先验定义的固定搜索空间，将创新约束在预定义的操作符内。尽管近期基于大语言模型的代码进化框架将固定搜索空间目标转向开放式程序空间，但其主要依赖标量指标（如NDCG、命中率），无法提供模型失效的定性分析或改进的定向指导。为此，我们提出Self-EvolveRec——一种通过集成用户模拟器（用于定性评估）与模型诊断工具（用于定量内部验证）来建立定向反馈循环的新型框架。此外，我们引入诊断工具-模型协同进化策略，以确保评估标准随推荐架构的演进而动态调整。大量实验表明，Self-EvolveRec在推荐性能和用户满意度方面均显著优于最先进的神经架构搜索及大语言模型驱动的代码进化基线方法。代码已开源：https://github.com/Sein-Kim/self_evolverec。