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    <abstract>Existing dialogue systems rely on query suggestion to enhance user engagement. Recent approaches mainly optimize generative models using click-through rate (CTR) models to align with user preferences. However, these methods are less effective in early-stage deployment scenarios, where click feedback is sparse and insufficient for training a reliable CTR model. To bridge this gap, we propose QualEQS, a quality-first iterative reinforcement learning framework for e-commerce query suggestion. We formalize actionable suggestion quality along three dimensions that directly affect downstream usability: answerability, factuality, and information gain. To continuously improve from online traffic without click supervision, we further propose group-level disagreement among candidate suggestions to identify ambiguous query contexts and mine hard training cases for iterative refinement. We also introduce EQS-Benchmark, a dataset of 16,949 real-world e-commerce queries for offline training and evaluation. Experiments show that our quality-based offline metrics correlate strongly with online performance, providing a practical evaluation recipe for sparse-feedback deployment. In both offline and online settings, QualEQS consistently outperforms strong baselines, yielding a 6.81% improvement in online ChatPV in a real-world enterprise-level conversational shopping assistant system.</abstract>
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%0 Conference Proceedings
%T Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion
%A Sun, Qi
%A Xiao, Kejun
%A Zhao, Huaipeng
%A Luo, Tao
%A Zeng, Xiaoyi
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F sun-etal-2026-quality
%X Existing dialogue systems rely on query suggestion to enhance user engagement. Recent approaches mainly optimize generative models using click-through rate (CTR) models to align with user preferences. However, these methods are less effective in early-stage deployment scenarios, where click feedback is sparse and insufficient for training a reliable CTR model. To bridge this gap, we propose QualEQS, a quality-first iterative reinforcement learning framework for e-commerce query suggestion. We formalize actionable suggestion quality along three dimensions that directly affect downstream usability: answerability, factuality, and information gain. To continuously improve from online traffic without click supervision, we further propose group-level disagreement among candidate suggestions to identify ambiguous query contexts and mine hard training cases for iterative refinement. We also introduce EQS-Benchmark, a dataset of 16,949 real-world e-commerce queries for offline training and evaluation. Experiments show that our quality-based offline metrics correlate strongly with online performance, providing a practical evaluation recipe for sparse-feedback deployment. In both offline and online settings, QualEQS consistently outperforms strong baselines, yielding a 6.81% improvement in online ChatPV in a real-world enterprise-level conversational shopping assistant system.
%U https://aclanthology.org/2026.emnlp-industry.45/
%P 751-762

Markdown (Informal)

[Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion](https://aclanthology.org/2026.emnlp-industry.45/) (Sun et al., EMNLP 2026)

• Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion (Sun et al., EMNLP 2026)

ACL

• Qi Sun, Kejun Xiao, Huaipeng Zhao, Tao Luo, and Xiaoyi Zeng. 2026. Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 751–762, Budapest, Hungary. Association for Computational Linguistics.