CTR-Guided Generative Query Suggestion in Conversational Search

TL;DR

This paper introduces GQS, a novel generative framework for conversational search query suggestions that leverages click modeling and preference optimization to improve relevance, diversity, and user engagement.

Contribution

GQS integrates multi-source CTR modeling, diversity-aware preference alignment, and iterative optimization to enhance query suggestion quality in conversational search.

Findings

GQS significantly improves CTR over baselines.

GQS enhances relevance and diversity of suggestions.

GQS outperforms existing methods on real-world tasks.

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.