自进化推荐系统：基于大语言模型定向反馈的自演进推荐算法

摘要

传统推荐系统自动化设计方法（如神经架构搜索NAS）通常受限于人为预设的固定搜索空间，其创新性被束缚于预定义算子范畴。尽管近期基于大语言模型的代码演化框架将目标转向开放式程序空间，但这些方法主要依赖标量指标（如NDCG、命中率），无法提供模型失效的定性分析或改进方向指引。为此，我们提出Self-EvolveRec框架，通过集成用户模拟器进行质性批判与模型诊断工具实施定量内部验证，构建定向反馈循环。此外，我们引入诊断工具-模型协同进化策略，确保评估标准随推荐架构演进动态调整。大量实验表明，Self-EvolveRec在推荐性能和用户满意度上显著优于最先进的NAS及大语言模型驱动代码演化基线方法。代码已开源：https://github.com/Sein-Kim/self_evolverec。

English

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.

自进化推荐系统：基于大语言模型定向反馈的自演进推荐算法

Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback

摘要

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