arXiv Machine Learning September 7, 2026 By Longshaokan Wang, Wai Tsang Keung, Punit Ghodasara, Roman Wang, Ali Dashti, Francesc Moreno-Noguer

Efficient Clustering with Quality Guardrails for LLM-based Recommender Systems at Industry Scale

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The paper presents a scalable two‑stage clustering algorithm that guarantees per‑sample quality guardrails for large‑scale LLM‑based recommender systems. By first forming Mini‑batch K‑Means clusters and then greedily selecting representatives that meet user‑specified similarity and attribute constraints, the method ensures each sample inherits only relevant and safe outputs. Benchmarks show the approach runs faster and scales to millions of inputs, achieving a 50‑fold reduction in downstream LLM cost and runtime while maintaining personalization.

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