自演进推荐系统：基于LLM定向反馈的自演化推荐框架

摘要

传统基于神经架构搜索（NAS）的推荐系统自动化设计方法通常受限于人为预设的固定搜索空间，导致创新范围被束缚于预定义算子。尽管近期基于大语言模型的代码演化框架将固定搜索空间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创新框架，通过集成用户模拟器（提供定性评估）与模型诊断工具（实现定量内部验证），构建具有方向性的反馈循环机制。此外，我们引入诊断工具与模型协同进化策略，确保评估标准能随推荐架构演化而动态调整。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当前最先进的NAS与LLM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.

自演进推荐系统：基于LLM定向反馈的自演化推荐框架

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

摘要

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