在Airbnb转变位置检索：从启发式到强化学习的旅程

How Airbnb leverages machine learning and reinforcement learning techniques to solve a unique information retrieval task in order to provide guests with unique, affordable, and differentiated accommodations around the world.

Airbnb 如何利用机器学习和强化学习技术解决独特的信息检索任务，以便为全球的客人提供独特、实惠和差异化的住宿。

By: Dillon Davis, Huiji Gao, Thomas Legrand, Weiwei Guo, Malay Haldar, Alex Deng, Han Zhao, Liwei He, Sanjeev Katariya

作者： Dillon Davis, Huiji Gao, Thomas Legrand, Weiwei Guo, Malay Haldar, Alex Deng, Han Zhao, Liwei He, Sanjeev Katariya

Introduction

介绍

Airbnb has transformed the way people travel around the globe. As Airbnb’s inventory spans diverse locations and property types, providing guests with relevant options in their search results has become increasingly complex. In this blog post, we’ll discuss shifting from using simple heuristics to advanced machine learning and reinforcement learning techniques to transform what we call location retrieval in order to address this challenge.

Airbnb 改变了人们在全球范围内旅行的方式。由于 Airbnb 的库存涵盖了各种地点和房产类型，为客人在搜索结果中提供相关选项变得越来越复杂。在这篇博客文章中，我们将讨论从使用简单的启发式方法转向高级机器学习和强化学习技术，以解决我们称之为位置检索的挑战。

The Challenge of Location Retrieval

位置检索的挑战

Guests typically start searching by entering a destination in the search bar and expect the most relevant results to be surfaced. These destinations can be countries, states, cities, neighborhoods, streets, addresses, or points of interest. Unlike traditional travel accommodations, Airbnb listings are spread across different neighborhoods and surrounding areas. For example, a family searching for a vacation rental in San Francisco might find better options in nearby cities like Daly City, where there are larger single-family homes. Thus, the system needs to account for not just the searched location but also nearby areas that might offer better options for the guest. This is evidenced by the locations of booked listings when searching for San Francisco shown below.

客人通常通过在搜索栏中输入目的地开始搜索，并期望显示最相关的结果。这些目的地可以是国家、州、市、社区、街道、地址或兴趣点。与传统的旅行住宿不同，Airbnb的房源分布在不同的社区和周边地区。例如，一个寻找旧金山度假租赁的家庭可能会在附近...