Airbnb search must balance a worldwide, highly varied supply of homes with guests whose location, amenity, style, and price expectations differ widely. Meeting those expectations hinges on an efficient retrieval stage that surfaces only the listings a guest might realistically book, before resource intensive ranking models are applied to determine the best results. Unlike many recommendation engines, our system faces a distinctive challenge, location retrieval, that sits upstream of ranking and determines which geographic areas are queried in order to filter inventory to a candidate set. The preexisting approach employs a deep bayesian bandit based system to predict a rectangular retrieval bounds area that can be used for filtering. The purpose of this paper is to demonstrate the methodology, challenges, and impact of rearchitecting search to retrieve from the subset of most bookable high precision rectangular map cells defined by dividing the world into 25M uniform cells.

翻译：Airbnb搜索平台必须平衡全球范围内高度多样化的房源供给与地理位置、设施偏好、风格要求和价格预期各不相同的房客需求。满足这些需求的关键在于高效的检索阶段——该阶段需在应用计算资源密集的排序模型确定最佳结果之前，仅呈现房客实际可能预订的房源列表。与众多推荐引擎不同，本系统面临一个位于排序上游的独特挑战——地理位置检索，该环节通过确定查询的地理区域来将库存筛选为候选集。现有方法采用基于深度贝叶斯赌博机的系统来预测可用于筛选的矩形检索边界区域。本文旨在阐述通过将全球划分为2500万个均匀单元，重新构建搜索架构以从最具预订价值的高精度矩形地图单元子集中进行检索的方法论、挑战与实际影响。