Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment
Xinpeng Liu, Lu Ma, Jiayi Qiao, Mengyu Zhou, Linglong Li, Xiaofeng Bian, Haonan Chen, Xiaoxi Jiang, Guanjun Jiang
Abstract
Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challenge is to generate slates whose individual queries are useful while the slate covers distinct intents. We propose an Intent-Driven Query Suggestion Framework with dual-stage optimization. First, intent-aware diversity modeling constructs intent-aligned supervised fine-tuning (SFT) data and uses an Intent-Aware Diversity Reward to optimize intent coverage. Second, query-level credit assignment routes individual quality signals to the corresponding query tokens while sharing a slate-level diversity signal across the slate. Experiments on a large-scale production dataset, including online A/B testing and offline evaluation, show improvements in click-through rate, query quality, and intent coverage.