AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment

• URL: http://arxiv.org/abs/2510.08081v1
• Date: Thu, 09 Oct 2025 11:11:02 GMT
• Title: AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
• Authors: Xiaochong Lan, Jie Feng, Yinxing Liu, Xinlei Shi, Yong Li,
• Abstract summary: AutoQual is a framework for transforming tacit knowledge embedded in data into explicit, computable features.<n>We deploy our method on a large-scale online platform with a billion-level user base.
• Score: 9.378765665099573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ranking online reviews by their intrinsic quality is a critical task for e-commerce platforms and information services, impacting user experience and business outcomes. However, quality is a domain-dependent and dynamic concept, making its assessment a formidable challenge. Traditional methods relying on hand-crafted features are unscalable across domains and fail to adapt to evolving content patterns, while modern deep learning approaches often produce black-box models that lack interpretability and may prioritize semantics over quality. To address these challenges, we propose AutoQual, an LLM-based agent framework that automates the discovery of interpretable features. While demonstrated on review quality assessment, AutoQual is designed as a general framework for transforming tacit knowledge embedded in data into explicit, computable features. It mimics a human research process, iteratively generating feature hypotheses through reflection, operationalizing them via autonomous tool implementation, and accumulating experience in a persistent memory. We deploy our method on a large-scale online platform with a billion-level user base. Large-scale A/B testing confirms its effectiveness, increasing average reviews viewed per user by 0.79% and the conversion rate of review readers by 0.27%.

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