The goal of Airbnb search is to match guests with the ideal accommodation that fits their travel needs. This is a challenging problem, as popular search locations can have around a hundred thousand available homes, and guests themselves have a wide variety of preferences. Furthermore, the launch of new product features, such as \textit{flexible date search,} significantly increased the number of eligible homes per search query. As such, there is a need for a sophisticated retrieval system which can provide high-quality candidates with low latency in a way that integrates with the overall ranking stack. This paper details our journey to build an efficient and high-quality retrieval system for Airbnb search. We describe the key unique challenges we encountered when implementing an Embedding-Based Retrieval (EBR) system for a two sided marketplace like Airbnb -- such as the dynamic nature of the inventory, a lengthy user funnel with multiple stages, and a variety of product surfaces. We cover unique insights when modeling the retrieval problem, how to build robust evaluation systems, and design choices for online serving. The EBR system was launched to production and powers several use-cases such as regular search, flexible date and promotional emails for marketing campaigns. The system demonstrated statistically-significant improvements in key metrics, such as booking conversion, via A/B testing.

翻译：Airbnb搜索的目标是为房客匹配符合其旅行需求的理想住宿。这是一个具有挑战性的问题，因为热门搜索地点的可用房源可能多达数十万套，且房客自身的偏好也多种多样。此外，新产品功能（例如\textit{灵活日期搜索}）的推出，显著增加了每次搜索查询的合格房源数量。因此，需要一个能够与整体排序系统集成、以低延迟提供高质量候选房源的复杂检索系统。本文详细介绍了我们为Airbnb搜索构建高效、高质量检索系统的历程。我们描述了在为Airbnb这样的双边市场实施基于嵌入的检索（EBR）系统时遇到的关键独特挑战——例如库存的动态性、包含多个阶段的冗长用户漏斗，以及多样化的产品界面。我们涵盖了在建模检索问题时的独特见解、如何构建稳健的评估系统，以及在线服务的设计选择。该EBBR系统已投入生产，并支持多种应用场景，如常规搜索、灵活日期搜索和营销活动的促销邮件。通过A/B测试，该系统在关键指标（例如预订转化率）上展示了统计显著的提升。