# CTR-Guided Generative Query Suggestion in Conversational Search

arXiv: https://arxiv.org/abs/2507.04072 · HTML: https://mlsanity.com/paper/2507.04072/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2025.

## Built on by (most cited first, 9 of 9)

- [Optimizing Generative Ranking Relevance via Reinforcement Learning in Xiaohongshu Search](https://mlsanity.com/paper/2512.00968/) (2025)
- [From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational System](https://mlsanity.com/paper/2508.15811/) (2025)
- [What to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems](https://mlsanity.com/paper/2608.07565/) (2026)
- [Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target](https://mlsanity.com/paper/2605.18899/) (2026)
- [OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations](https://mlsanity.com/paper/2605.23668/) (2026)
- [Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment](https://mlsanity.com/paper/2609.19209/) (2026)
- [AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation](https://mlsanity.com/paper/2603.19710/) (2026)
- [Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion](https://mlsanity.com/paper/2603.22922/) (2026)
- [How Can Recommendation Feedback Evolve Agent Memory?](https://mlsanity.com/paper/2609.37544/) (2026)

## Abstract (arXiv)

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.