Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback
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.
6992c3b650fb2c0be47839b7
Sein Kim
claimed_verified
2026-02-16T15:36:07.138Z
670e3f745e6b07cedfb76001
/avatars/b4bbe8a0bffc25cf4a4a5543e10b3655.svg
Sein Kim
Sein-Kim
user
Sein-Kim
6992c3b650fb2c0be47839b8
Sangwu Park
6992c3b650fb2c0be47839b9
Hongseok Kang
6992c3b650fb2c0be47839ba
Wonjoong Kim
6992c3b650fb2c0be47839bb
Jimin Seo
6992c3b650fb2c0be47839bc
Yeonjun In
6992c3b650fb2c0be47839bd
Kanghoon Yoon
6992c3b650fb2c0be47839be
Chanyoung Park
Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback
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.