Problem
Research questions and friction points this paper is trying to address.
Generative Query Suggestion
Intent Coverage
User Engagement
Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment
🤖 AI Summary
该研究通过意图驱动的查询建议框架解决生成有用且覆盖不同意图的查询列表问题，采用双阶段优化方法提高用户参与度。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Intent-Driven Query Suggestion
Dual-Stage Optimization
Intent-Aware Diversity Reward
Query-Level Credit Assignment
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