{"ID":23475569,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.19209","arxiv_id":"2609.19209","title":"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.","short_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-st...","url_abs":"https://arxiv.org/abs/2609.19209","url_pdf":"https://arxiv.org/pdf/2609.19209v1","authors":"[\"Xinpeng Liu\",\"Lu Ma\",\"Jiayi Qiao\",\"Mengyu Zhou\",\"Linglong Li\",\"Xiaofeng Bian\",\"Haonan Chen\",\"Xiaoxi Jiang\",\"Guanjun Jiang\"]","published":"2026-09-16T11:19:06Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}