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

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.

Machine Learning (cs.LG)

arXiv:2609.19209 [cs.LG]

(or arXiv:2609.19209v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.19209 Focus to learn more arXiv-issued DOI via DataCite

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