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[2602.12612] Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback
Summary
The paper presents Self-EvolveRec, a framework for self-evolving recommender systems that utilizes LLM-based directional feedback to enhance performance and user satisfaction.
Why It Matters
This research addresses limitations in traditional recommender systems by introducing a feedback loop that combines qualitative and quantitative evaluations. It offers a novel approach to improving recommendation algorithms, which is crucial for enhancing user experience in various applications.
Key Takeaways
- Self-EvolveRec integrates user feedback and model diagnostics for improved recommender systems.
- The framework adapts evaluation criteria dynamically as the recommendation architecture evolves.
- Extensive experiments show significant performance improvements over existing methods.
Computer Science > Information Retrieval arXiv:2602.12612 (cs) [Submitted on 13 Feb 2026] Title:Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback Authors:Sein Kim, Sangwu Park, Hongseok Kang, Wonjoong Kim, Jimin Seo, Yeonjun In, Kanghoon Yoon, Chanyoung Park View a PDF of the paper titled Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback, by Sein Kim and 7 other authors View PDF HTML (experimental) Abstract: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-drive...
