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

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arxiv 2602.12612 v2 pith:CBFLNGWF submitted 2026-02-13 cs.IR cs.AI

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

Sein Kim , Sangwu Park , Hongseok Kang , Wonjoong Kim , Jimin Seo , Yeonjun In , Kanghoon Yoon , Hyunsik Jeon
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Chanyoung Park
This is my paper
classification cs.IR cs.AI
keywords codedirectionalmodelsearchself-evolverecarchitecturediagnosisevolution
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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