Sponsored search is a key revenue source for search engines, where advertisers bid on keywords to target users or search queries of interest. However, finding relevant keywords for a given query is challenging due to the large and dynamic keyword space, ambiguous user/advertiser intents, and diverse possible topics and languages. In this work, we present a comprehensive comparison between two paradigms for online query rewriting: Generative (NLG) and Dense Retrieval (DR) methods. We observe that both methods offer complementary benefits that are additive. As a result, we show that around 40% of the high-quality keywords retrieved by the two approaches are unique and not retrieved by the other. To leverage the strengths of both methods, we propose CLOVER-Unity, a novel approach that unifies generative and dense retrieval methods in one single model. Through offline experiments, we show that the NLG and DR components of CLOVER-Unity consistently outperform individually trained NLG and DR models on public and internal benchmarks. Furthermore, we show that CLOVER-Unity achieves 9.8% higher good keyword density than the ensemble of two separate DR and NLG models while reducing computational costs by almost half. We conduct extensive online A/B experiments on Microsoft Bing in 140+ countries and achieve improved user engagement, with an average increase in total clicks by 0.89% and increased revenue by 1.27%. We also share our practical lessons and optimization tricks for deploying such unified models in production.

翻译：赞助搜索是搜索引擎的重要收入来源，广告主通过关键词竞价来定位目标用户或感兴趣的搜索查询。然而，面对庞大且动态变化的关键词空间、模糊的用户/广告主意图以及多样化的主题和语言，为给定查询找到相关关键词颇具挑战。本文系统比较了两种在线查询改写范式：生成式（NLG）方法与稠密检索（DR）方法。我们观察到两种方法具有互补优势且效果可叠加，约40%的高质量关键词仅被单一方法检索到。为融合两者优势，我们提出CLOVER-Unity——一种将生成式与稠密检索方法统一于单个模型中的创新方案。离线实验表明，CLOVER-Unity中的NLG与DR组件在公开及内部基准测试中均持续优于独立训练的NLG和DR模型。此外，与两个独立DR和NLG模型的集成方案相比，CLOVER-Unity在将计算成本降低近一半的同时，使优质关键词密度提升9.8%。我们在微软必应（覆盖140多个国家/地区）开展了广泛线上A/B实验，用户参与度显著提升，总点击量平均增加0.89%，收入增长1.27%。同时，本文分享了此类统一模型在实际部署中的实践经验和优化技巧。