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The paper introduces Self-EvolveRec, a framework for self-evolving recommender systems that uses LLMs to explore open-ended program spaces beyond fixed human-defined search spaces. It establishes a directional feedback loop using a User Simulator for qualitative critiques and a Model Diagnosis Tool for quantitative internal verification, guiding the LLM's code evolution process. Experiments show Self-EvolveRec outperforms NAS and LLM-driven code evolution baselines in recommendation performance and user satisfaction by dynamically adapting evaluation criteria with a Diagnosis Tool - Model Co-Evolution strategy.
Forget hand-tuning recommender systems: Self-EvolveRec uses LLMs and a novel directional feedback loop to automatically evolve better architectures.
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.
