发表机构
Peking University; Qwen Business Unit of Alibaba; National University of Singapore; Pengcheng National Laboratory（北京大学; 阿里巴巴通义千问事业部; 新加坡国立大学; 鹏城国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生成式查询建议中意图覆盖与查询质量平衡的挑战，提出意图驱动框架，通过双阶段优化（意图感知多样性建模与查询级信用分配）提升点击率、查询质量和意图覆盖。
AI 中文摘要
生成式查询建议旨在通过预测用户意图并推荐相关的后续查询来增强用户参与度。一个核心挑战是生成这样的候选列表：其中单个查询有用，同时整个列表覆盖不同的意图。我们提出了一种具有双阶段优化的意图驱动查询建议框架。首先，意图感知的多样性建模构建了与意图对齐的监督微调（SFT）数据，并使用意图感知的多样性奖励来优化意图覆盖。其次，查询级信用分配将个体质量信号路由到相应的查询令牌，同时在列表级别共享多样性信号。在大规模生产数据集上的实验，包括在线A/B测试和离线评估，显示了点击率、查询质量和意图覆盖率的提升。
英文摘要
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.
