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公司:Airbnb

关联话题: 爱彼迎

爱彼迎(英语:Airbnb)是一个出租住宿民宿的网站,提供短期出租房屋或房间,让旅行者通过网站或手机发掘和预订世界各地的独特房源,为近年来共享经济发展的代表之一。该网站成立于2008年8月,公司总部位于美国加利福尼亚州旧金山,是一家私有公司,由“Airbnb, Inc.”负责管理营运。目前,爱彼迎在191个国家,65,000个城市中共有400万名房东、超过3,000,000笔房源。

该公司在中国的品牌名为爱彼迎,取“让爱彼此相迎”之义,品牌名发布后被批评“难听”和“性暗示”。

用户必须注册互联网账号才能使用网站。每一个住宿物件都与一位房东链接,房东的个人文件包括其他用户的推荐、顾客评价、回复评等和私信系统。

Adopting Bazel for Web at Scale

How and Why We Migrated Airbnb’s Large-Scale Web Monorepo to Bazel.

Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning

How Airbnb leverages machine learning and reinforcement learning techniques to solve a unique information retrieval task in order to…

Automation Platform v2: Improving Conversational AI at Airbnb

How Airbnb’s conversational AI platform powers LLM application development.

Sandcastle: data/AI apps for everyone

Airbnb made it easy to bring data/AI ideas to life through a platform for prototyping web applications.

爱彼迎以用户体验驱动的 Android 性能度量

爱彼迎的整个用户旅程被划分为不同的页面,每个页面都对其自己的PPS值进行测量。为了支持这个基于页面的性能跟踪系统,我们构建了一个标准化的基础架构,使工程师能够配置代表其功能的页面。

在Android上,每个页面都与一个Fragment相关联。每个Fragment都必须提供一个LoggingConfig对象,指定一个页面名称,以便在需要引用页面名称时能够检索到。我们在Fragment的生命周期中收集性能数据,并在Fragment暂停时才发出日志事件。

我们用一个通用的PageName枚举类型标识每个页面,并在所有平台上引用,从而一致地表示我们用户操作中的每个页面。

Riverbed Data Hydration — Part 1

A deep dive into the streaming aspect of the Lambda architecture framework of Riverbed.

爱彼迎以用户体验驱动的 iOS 性能度量

爱彼迎上的整个用户旅程被划分为不同的页面,每个页面都有自己的页面性能分数(PPS)。为了支持这个基于页面的性能跟踪系统,我们建立了一个标准化的基础设施,使工程师能够配置页面和功能的对应关系。

在 iOS 上,页面与 UIViewController相关联。我们在 UIViewController 的生命周期中收集性能数据,在 viewDidDisappear 时发送收集到的日志。我们使用 PageName 作为全局的页面标识符,日志必须存在 PageName 时才能创建或发送。

Building Postcards for “Airbnb” Scale

How the Airbnb Media team built group travel Postcards for the 2024 Summer Release by leveraging a novel destination matching algorithm while advancing the platform’s image & localized text processing capabilities.

Personal Data Classification

An Important Foundation For Security, Privacy, and Compliance at Airbnb.

Apache Flink® on Kubernetes

At Airbnb, Apache Flink was introduced in 2018 as a supplementary solution for stream processing. It ran alongside Apache Spark™ Streaming for several years before transitioning to become the primary stream processing platform. In this blog post, we will delve into the evolution of Flink architecture at Airbnb and compare our prior Hadoop Yarn platform with the current Kubernetes-based architecture. Additionally, we will discuss the efforts undertaken throughout the migration process and explore the challenges that arose during this journey. In the end we will summarize the impact, learnings along the way and future plans.

How Airbnb Smoothly Upgrades React

Incrementally modernizing our frontend infrastructure to roll out the latest React features without downgrades.

Rethinking Text Resizing on Web

Airbnb has made significant strides in improving web accessibility for Hosts and guests who require larger text sizes.

Animations: Bringing the Host Passport to Life on iOS

How Airbnb enabled hosts and guests to connect and introduce themselves through the Host Passport.

Airbnb Brandometer: Powering Brand Perception Measurement on Social Media Data with AI

At Airbnb, we have developed Brandometer, a state-of-the-art natural language understanding (NLU) technique for understanding brand perception based on social media data. Brand perception refers to…

Introducing Trio | Part III

Part three on how we built a Compose based architecture with Mavericks in the Airbnb Android app.

Chronon, Airbnb’s ML Feature Platform, Is Now Open Source

A feature platform that offers observability and management tools, allows ML practitioners to use a variety of data sources, while handling the complexity of data engineering, and provides low latency streaming.

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