Journey Platform: Low-code notification workflow platform that allows technical and non-technical users to create complex workflows through a simple drag and drop user interface.

How Airbnb leverages AWS, Packer, and Terraform to update macOS on hundreds of Cl machines in hours instead of days.

A case study in service mesh performance optimization.

Airbnb 2022 release introduced Categories, a browse focused product that allows the user to seek inspiration by browsing collections of homes revolving around a common theme, such as Lakefront, Countryside, Golf, Desert, National Parks, Surfing, etc. In Part I of our Categories Blog Series we covered the high level approach to creating Categories and showcasing them in the product. In this Part II we will describe the ML Categorization work in more detail.

Throughout the post we use the Lakefront category as a running example to showcase the ML-powered category development process. Similar process was applied for other categories, with category specific nuances. For example, some categories rely more on points of interests, while others more on structured listing signals, image data, etc.

Airbnb connects millions of guests and Hosts everyday. Most of these connections are forged through search, the results of which are determined by a neural network–based ranking algorithm. While this neural network is adept at selecting individual listings for guests, we recently improved the neural network to better select the overall collection of listings that make up a search result. In this post, we dive deeper into this recent breakthrough that enhances the diversity of listings in search results.

At Airbnb, we have been consciously designing and building products to be equally usable by all users. Making our mobile apps and websites more accessible not only aligns with our company’s mission of creating a world where people can belong anywhere, but also supports the civil rights of people with disabilities and complies with the law.

In this article, we highlight some of the efforts we have made to make the app more accessible, for example, labeling UI elements, grouping related content, supporting large font scale, providing heading and page names. The Airbnb app is one of the most popular travel apps with millions of users and supports many features. Making such a complex app more accessible is a huge endeavor that we are continuously working on.

在 2020 年 Hasura 的企业 GraphQL 会议上，Airbnb爱彼迎 展示了 Viaduct，一个面向数据的服务网格（Service Mesh） ，其为爱彼迎基于微服务的 SOA（Service-Oriented Architecture）的模块化带来了阶梯性的提升。我们将在这篇文章中介绍 Viaduct 背后的思想及大致运作原理。

How Airbnb is applying declarative design patterns to rapidly build fluid transition animations.

How Airbnb uses visual attributes to enhance the Guest and Host experience.

Leveraging text generation models to build more effective, scalable customer support products.

Online travel search hasn’t changed much in the last 25 years. The traveler enters her destination, dates, and the number of guests into a search interface, which dutifully returns a list of options that best meet the criteria. Eventually, Airbnb and other travel sites made improvements to allow for better filtering, ranking, personalization and, more recently, to display results slightly outside of the specified search parameters–for example, by accommodating flexible dates or by suggesting nearby locations. Taking a page from the travel agency model, these websites also built more “inspirational” browsing experiences that recommend popular destinations, showcasing these destinations with captivating imagery and inventory (think digital “catalog”).

分类系统是用于对信息进行分类和组织的知识组织系统。分类系统使用文字（而不是数字或符号）来描述事物，并使用层次结构来将事物进行分类。分类系统的结构反映了这些事物是如何相互关联的。例如，超赞房东是房东的一种类型，而房东是爱彼迎用户的一种类型。分类系统提供了重要的术语控制，使下游系统能够通过其定位信息以及分析一致的、结构化的数据。

爱彼迎在前端产品中使用分类系统来帮助房客和房东发现优质的住宿、体验、内容以及客户支持产品。爱彼迎也在后台工具中使用分类系统来结构化数据、组织内部信息以及支持机器学习应用程序。

Introduction of Airbnb interleaving experimentation framework, usage and approaches to address challenges in our unique business.

How Airbnb built a persistent, high availability and low latency key-value storage engine for accessing derived data from offline and streaming events.

This blog aims to introduce Airbnb’s experience upgrading Data Warehouse infrastructure to Spark and Iceberg.

In our first post we discussed the need for a near real time Safe Deploy system and some of the statistics that power its decisions. In this post we will cover the architecture and engineering choices behind the various components that Safe Deploys comprises.

Designing a near real-time experimentation system required making explicit tradeoffs among speed, precision, cost, and resiliency. An early decision was to limit near real-time results to only the first 24 hours of an experiment — enough time to catch any major issues and transition to using comprehensive results from the batch pipeline. The idea being once batch results were available, experimenters would no longer need real time results. The following sections describe the additional design decisions in each component of the Safe Deploys system.