用城市偏好增强的提示，让大模型更懂本地需求。
LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services
- 用共现词挖掘城市偏好，动态注入提示而非修改参数
- 线上点击率提升0.35%，无结果率降低3.98%
- 适合需要快速响应本地变化的服务平台
在本地生活服务平台中，查询建议通过从输入前缀生成候选查询来降低用户操作成本。传统多阶段系统高度依赖历史热门查询，难以捕捉长尾和新兴需求。尽管大语言模型具备强大的语义泛化能力，但在本地生活服务中的部署面临三大挑战：城市偏好感知不足、偏好优化中的暴露偏差以及严格的在线延迟约束。本文提出 LocalSUG，一个面向本地生活服务的基于大语言模型的查询建议框架。LocalSUG 通过词共现挖掘城市偏好增强的候选，将其作为动态参考注入提示，而非融合进模型参数，使模型能适应城市偏好变化（如商家开业或关闭），减少过时或本地无效建议。进一步提出基于束搜索的 GRPO 算法，对齐训练与推理时的解码过程，并联合优化相关性与业务导向奖励。最后，引入质量感知的束搜索加速和词汇表剪枝，在保持生成质量的同时降低在线延迟。离线评估与大规模线上 A/B 测试表明，LocalSUG 将点击率提升 +0.35%，无结果率降低 3.98%，验证了其在真实场景下的有效性。
原文摘要 · Abstract (English)
In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historical popular queries, limiting their ability to capture long-tail and emerging demand. Although LLMs provide strong semantic generalization, their deployment in local-life services faces three challenges: insufficient city-preference awareness, exposure bias in preference optimization, and strict online latency constraints. We propose LocalSUG, an LLM-based query suggestion framework for local-life services. LocalSUG mines city-preference-enhanced candidates from term co-occurrence and injects them into prompts as dynamic references rather than fusing them into model parameters. This allows the model to adapt to changing city preferences, such as merchant openings or closures, while reducing stale or locally invalid suggestions. We further introduce a beam-search-driven GRPO algorithm to align training with inference-time decoding and optimize relevance together with business-oriented rewards. Finally, quality-aware beam acceleration and vocabulary pruning reduce online latency while preserving generation quality. Offline evaluations and large-scale online A/B testing show that LocalSUG improves CTR by +0.35% and reduces the no-result rate by 3.98%, demonstrating its effectiveness in real-world deployment.
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