@inproceedings{min-etal-2025-ctr,
title = "{CTR}-Guided Generative Query Suggestion in Conversational Search",
author = "Min, Erxue and
Huang, Hsiu-Yuan and
Yang, Xihong and
Yang, Min and
Jia, Xin and
Wu, Yunfang and
Cai, Hengyi and
Wang, Junfeng and
Wang, Shuaiqiang and
Yin, Dawei",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.178/",
pages = "2624--2634",
ISBN = "979-8-89176-333-3",
abstract = "Generating effective query suggestions in conversational search requires aligning model outputs with user click preferences. However, directly optimizing for these preferences is difficult because click signals are sparse and inherently noisy. To address this, we propose Generative Query Suggestion (GQS), a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.GQS consists of three key components: (1) a \textit{Multi-Source CTR Modeling} module that captures diverse contextual signals to estimate fine-grained click-through rates, thereby constructing more reliable user click-preference pairs; (2) a \textit{Diversity-Aware Preference Alignment} strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a \textit{CTR-Calibrated Iterative Optimization} process that jointly refines both the CTR model and the query suggestion model across training rounds, enabling effective data reuse.Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity."
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<abstract>Generating effective query suggestions in conversational search requires aligning model outputs with user click preferences. However, directly optimizing for these preferences is difficult because click signals are sparse and inherently noisy. To address this, we propose Generative Query Suggestion (GQS), a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates, thereby constructing more reliable user click-preference pairs; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a CTR-Calibrated Iterative Optimization process that jointly refines both the CTR model and the query suggestion model across training rounds, enabling effective data reuse.Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.</abstract>
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%0 Conference Proceedings
%T CTR-Guided Generative Query Suggestion in Conversational Search
%A Min, Erxue
%A Huang, Hsiu-Yuan
%A Yang, Xihong
%A Yang, Min
%A Jia, Xin
%A Wu, Yunfang
%A Cai, Hengyi
%A Wang, Junfeng
%A Wang, Shuaiqiang
%A Yin, Dawei
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F min-etal-2025-ctr
%X Generating effective query suggestions in conversational search requires aligning model outputs with user click preferences. However, directly optimizing for these preferences is difficult because click signals are sparse and inherently noisy. To address this, we propose Generative Query Suggestion (GQS), a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates, thereby constructing more reliable user click-preference pairs; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a CTR-Calibrated Iterative Optimization process that jointly refines both the CTR model and the query suggestion model across training rounds, enabling effective data reuse.Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.
%U https://aclanthology.org/2025.emnlp-industry.178/
%P 2624-2634
Markdown (Informal)
[CTR-Guided Generative Query Suggestion in Conversational Search](https://aclanthology.org/2025.emnlp-industry.178/) (Min et al., EMNLP 2025)
ACL
- Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, and Dawei Yin. 2025. CTR-Guided Generative Query Suggestion in Conversational Search. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2624–2634, Suzhou (China). Association for Computational Linguistics.