将全球划分为2500万个地图单元，精准定位高预订潜力区域以提升搜索效率
High Precision Audience Expansion via Extreme Classification in a Two-Sided Marketplace
- 将世界划分为2500万个均匀地图单元，筛选高预订可能性的候选区域
- 相比原有方法，检索精度显著提升，有效减少无效候选集
- 适用于大规模地理检索场景，尤其适合双边市场平台优化搜索性能
Airbnb搜索需在海量、多样化的房源与游客的地理位置、设施、风格及价格偏好之间取得平衡。满足用户期望的关键在于高效检索阶段，仅筛选出游客可能实际预订的房源，再由资源密集型排序模型确定最优结果。与多数推荐系统不同，本系统面临上游的定位检索挑战，决定查询哪些地理区域以过滤库存形成候选集。现有方法采用基于深度贝叶斯赌博机的系统预测矩形检索范围。本文旨在展示重构搜索系统的流程、挑战及其影响：通过将全球划分为2500万个均匀地图单元，从其中筛选出最可能被预订的高精度矩形单元作为检索目标，从而优化检索效率。
原文摘要 · Abstract (English)
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.
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