The Airbnb search system grapples with many unique challenges as it continues to evolve. We oversee a marketplace that is nuanced by geography, diversity of homes, and guests with a variety of preferences. Crafting an efficient search system that can accommodate diverse guest needs, while showcasing relevant homes lies at the heart of Airbnb's success. Airbnb search has many challenges that parallel other recommendation and search systems but it has a unique information retrieval problem, upstream of ranking, called location retrieval. It requires defining a topological map area that is relevant to the searched query for homes listing retrieval. The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. Despite the lack of suitable, prevalent machine learning based approaches, we tackle cold start, generalization, differentiation and algorithmic bias. We detail the efficacy of heuristics, statistics, machine learning, and reinforcement learning approaches to solve these challenges, particularly for systems that are often unexplored by current literature.

翻译：Airbnb搜索系统在持续演进过程中面临着诸多独特挑战。我们管理的市场平台具有地理复杂性、房源多样性以及用户偏好多元化的特点。构建能够满足多样化用户需求、同时精准展示相关房源的高效搜索系统，是Airbnb成功运营的核心所在。尽管Airbnb搜索系统与其他推荐和搜索系统存在诸多共性挑战，但其在排序流程上游存在独特的信息检索问题——位置检索。该问题需要为房源列表检索定义与搜索查询相关的拓扑地图区域。本文旨在系统阐述构建基于机器学习的位置检索产品的方法论、挑战与实际影响。在缺乏成熟机器学习解决方案的情况下，我们成功应对了冷启动、泛化性、差异化及算法偏差等难题。我们详细论证了启发式方法、统计模型、机器学习及强化学习在解决这些挑战中的有效性，尤其针对当前文献较少涉足的系统类型进行了深入探讨。