Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment

A new arXiv paper (arXiv:2609.19209v1) proposes an Intent-Driven Query Suggestion Framework that combines intent-aware diversity modeling with query-level credit assignment to generate follow-up query slates. The framework builds intent-aligned supervised fine-tuning data and uses an Intent-Aware Diversity Reward to optimize intent coverage, while routing individual quality signals to query tokens and sharing a slate-level diversity signal across the slate. Experiments on a large-scale production dataset, including online A/B testing and offline evaluation, showed improvements in click-through rate, query quality, and intent coverage.

arXiv:2609.19209v1 Announce Type: new 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.