CTR-Guided Generative Query Suggestion in Conversational Search
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918084844978176 |
|---|---|
| author | Min, Erxue Huang, Hsiu-Yuan Yang, Xihong Yang, Min Jia, Xin Wu, Yunfang Cai, Hengyi Wang, Junfeng Wang, Shuaiqiang Yin, Dawei |
| author_facet | Min, Erxue Huang, Hsiu-Yuan Yang, Xihong Yang, Min Jia, Xin Wu, Yunfang Cai, Hengyi Wang, Junfeng Wang, Shuaiqiang Yin, Dawei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CTR-Guided Generative Query Suggestion in Conversational Search Min, Erxue Huang, Hsiu-Yuan Yang, Xihong Yang, Min Jia, Xin Wu, Yunfang Cai, Hengyi Wang, Junfeng Wang, Shuaiqiang Yin, Dawei Information Retrieval 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. |
| title | CTR-Guided Generative Query Suggestion in Conversational Search |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2507.04072 |