CTR-Guided Generative Query Suggestion in Conversational Search

• URL: http://arxiv.org/abs/2507.04072v1
• Date: Sat, 05 Jul 2025 15:32:41 GMT
• Title: CTR-Guided Generative Query Suggestion in Conversational Search
• Authors: Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin,
• Abstract summary: GQS is a generative framework that integrates click modeling and preference optimization to enhance real-world user engagement.<n>GQGS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO); and (3) a CTR-Calibrated Iterative Optimization process that jointly refines the CTR and generation models across training rounds.
• Score: 35.654879254147964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. We propose GQS, a generative framework that integrates click modeling and preference optimization to enhance 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; (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 the CTR and generation models across training rounds. Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.

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