在Airbnb构建用户信号平台

How Airbnb built a stream processing platform to power user personalization.

Airbnb 如何构建流处理平台以支持用户个性化。

By: Kidai Kwon, Pavan Tambay, Xinrui Hua, Soumyadip (Soumo) Banerjee, Phanindra (Phani) Ganti

作者： Kidai Kwon, Pavan Tambay, Xinrui Hua, Soumyadip (Soumo) Banerjee, Phanindra (Phani) Ganti

Overview

概述

Understanding user actions is critical for delivering a more personalized product experience. In this blog, we will explore how Airbnb developed a large-scale, near real-time stream processing platform for capturing and understanding user actions, which enables multiple teams to easily leverage real-time user activities. Additionally, we will discuss the challenges encountered and valuable insights gained from operating a large-scale stream processing platform.

理解用户行为对于提供更个性化的产品体验至关重要。在这篇博客中，我们将探讨 Airbnb 如何开发一个大规模、近实时的流处理平台来捕捉和理解用户行为，从而使多个团队能够轻松利用实时用户活动。此外，我们还将讨论在操作大规模流处理平台时遇到的挑战和获得的宝贵见解。

Background

背景

Airbnb connects millions of guests with unique homes and experiences worldwide. To help guests make the best travel decisions, providing personalized experiences throughout the booking process is essential. Guests may move through various stages — browsing destinations, planning trips, wishlisting, comparing listings, and finally booking. At each stage, Airbnb can enhance the guest experience through tailored interactions, both within the app and through notifications.

Airbnb 将数百万的客人与全球独特的房源和体验连接起来。为了帮助客人做出最佳的旅行决策，在预订过程中提供个性化体验至关重要。客人可能会经历不同的阶段——浏览目的地、计划旅行、加入愿望清单、比较房源，最终预订。在每个阶段，Airbnb 都可以通过应用内和通知中的定制互动来提升客人的体验。

This personalization can range from understanding recent user activities, like searches and viewed homes, to segmenting users based on their trip intent and stage. A robust infrastructure is essential for processing extensive user engagement data and delivering insights in near real-time. Additionally, it’s important to platformize the infrastructure so that other teams can contribute to deriving user insights, especially since many engineering teams are not familiar with stream processing.

这种个性化可以从了解最近的用户活动（如搜索和...