用AI自动生成推荐系统，还能根据用户反馈持续优化。
Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback
- 引入用户模拟器和诊断工具，实现定性反馈与定量验证结合
- 在MovieLens和Amazon datasets上推荐性能超越现有方法
- 适合对智能推荐系统迭代优化感兴趣的开发者
传统自动推荐系统设计方法（如NAS）受限于人工预设的搜索空间，创新仅限于预定义操作符。尽管近期基于大模型的代码演化框架将搜索空间扩展至开放程序空间，但主要依赖NDCG、命中率等标量指标，难以提供模型失败的定性分析或改进方向。为此，我们提出Self-EvolveRec，通过集成用户模拟器进行定性批判，结合模型诊断工具实现量化内部验证，构建定向反馈闭环。此外，提出模型共演化策略，使评估标准随推荐架构演进动态调整。大量实验表明，Self-EvolveRec在MovieLens和Amazon数据集上显著优于当前最先进的NAS及基于LLM的代码演化基线，在推荐性能与用户满意度方面均有提升。代码已开源。
原文摘要 · Abstract (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.
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