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Airbnb Technology Team

Official account of the Airbnb Technology Team, sharing Airbnb's tech innovations and real-world implementations, building a world where home is everywhere through technology.

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Latest from Airbnb Technology Team

46 recent articles

Airbnb Technology Team

Nov 17, 2023 · Mobile Development

Airbnb’s Journey from Epoxy/UIKit to SwiftUI: Evaluation, Migration, and Lessons Learned

Airbnb’s three‑stage migration from its custom Epoxy/UIKit stack to SwiftUI involved extensive evaluation, rebuilding the design system with flexible style protocols, bridging UIKit and SwiftUI, adopting one‑way data flow, extensive testing and training, resulting in six‑fold code reduction, comparable performance, widespread production adoption, and higher developer productivity despite remaining framework challenges.

AirbnbDeclarative UIEpoxy

0 likes · 17 min read

Airbnb’s Journey from Epoxy/UIKit to SwiftUI: Evaluation, Migration, and Lessons Learned

Airbnb Technology Team

Sep 22, 2023 · Backend Development

Improving Page Performance with HTTP Streaming: Airbnb’s Early Flush Implementation

Airbnb boosted page performance by implementing HTTP streaming with an early‑flush strategy that sends the head chunk first, allowing browsers to start loading CSS and JavaScript while the server fetches data, cutting First Contentful Paint by roughly 100 ms despite challenges like tag handling, buffering, and latency.

Early FlushHTTP streamingNode.js

0 likes · 11 min read

Improving Page Performance with HTTP Streaming: Airbnb’s Early Flush Implementation

Airbnb Technology Team

Aug 3, 2023 · Artificial Intelligence

Improving Airbnb Search Ranking Diversity with Neural Networks

Airbnb upgraded its neural‑network ranking system by adding a similarity network that penalizes duplicate‑like listings, enabling the algorithm to present a more diverse set of options, which boosted booking rates, value, and five‑star ratings, demonstrating that reduced result similarity improves overall search quality.

AirbnbDiversityMachine Learning

0 likes · 8 min read

Improving Airbnb Search Ranking Diversity with Neural Networks

Airbnb Technology Team

May 23, 2023 · Artificial Intelligence

Applying Text Generation Models to Scalable Customer Support at Airbnb

Airbnb replaced its XLM‑RoBERTa ranking with an MT5 encoder‑decoder for content recommendation, built a real‑time generative assistant for reply suggestions and intent detection, and deployed a T5‑based paraphrase chatbot, showing that large‑scale pre‑trained transformers improve relevance, agent efficiency, and user satisfaction.

AIAirbnbLanguage Models

0 likes · 12 min read

Applying Text Generation Models to Scalable Customer Support at Airbnb

Airbnb Technology Team

Mar 17, 2023 · Information Security

Airbnb Data Privacy and Security Engineering: Automated Data Protection Service Overview

Airbnb’s Data Protection Service unifies privacy and security metadata, offering APIs that automate annotation verification, export and IDL validation, data‑subject‑rights orchestration, and secret‑leak detection, while assigning ownership, minimizing manual effort, and ensuring global, consistent compliance across the platform.

AirbnbCI checksData Privacy

0 likes · 13 min read

Airbnb Data Privacy and Security Engineering: Automated Data Protection Service Overview

Airbnb Technology Team

Jan 11, 2023 · Cloud Native

Viaduct: A Data‑Oriented Service Mesh for Modern SOA

Viaduct, Airbnb’s data‑oriented service mesh built on GraphQL, replaces traditional procedural meshes by centralizing a schema that routes queries across thousands of micro‑services, enabling fine‑grained data access, serverless derived fields, automatic schema propagation, and improved observability, now handling most of Airbnb’s API traffic.

Data-Oriented ArchitectureGraphQLMicroservices

0 likes · 8 min read

Viaduct: A Data‑Oriented Service Mesh for Modern SOA

Airbnb Technology Team

Nov 3, 2022 · Artificial Intelligence

T-LEAF: A Taxonomy Learning and Evaluation Framework for Airbnb Community Support Classification System

The T‑LEAF framework introduces quantitative metrics for coverage, usefulness, and consistency to iteratively develop Airbnb’s unified Contact‑Reason taxonomy, enabling faster feedback loops, reducing “Other” classifications, and improving both human annotation agreement and machine‑learning prediction accuracy in production.

Evaluation FrameworkMachine Learningclassification

0 likes · 14 min read

T-LEAF: A Taxonomy Learning and Evaluation Framework for Airbnb Community Support Classification System

Airbnb Technology Team

Aug 26, 2022 · Information Security

Airbnb Data Privacy and Security Engineering: Inspekt Data Classification Service and Angmar Secret Detection

Airbnb’s second privacy‑security article describes how the Inspekt service automatically classifies personal and sensitive data across diverse stores using regexes, Aho‑Corasick tries, machine‑learning models and custom validators, measures validator quality, and how the Angmar system scans code repositories for secrets via CI checks and pre‑commit hooks, with plans to broaden coverage to more APIs and data stores.

Data PrivacyMachine LearningSecret Detection

0 likes · 16 min read

Airbnb Data Privacy and Security Engineering: Inspekt Data Classification Service and Angmar Secret Detection

Airbnb Technology Team

Aug 12, 2022 · User Experience Design

How to Design Effective Entry Points and UI for Intelligent Customer Service

This article examines the design of intelligent customer service interfaces, detailing dynamic and static entry modes, trigger mechanisms, queue systems, and the composition of system information, personalized features, chat operation, and input areas, while offering practical UI patterns and considerations for effective user interaction.

ChatbotCustomer ServiceEntry Points

0 likes · 15 min read

How to Design Effective Entry Points and UI for Intelligent Customer Service

Airbnb Technology Team

Jul 13, 2022 · Product Management

User Segmentation and Journey Mapping for Intelligent Customer Service: Insights for Positive and Negative Explorers

The article examines intelligent customer‑service user segmentation into positive and negative explorers, maps their three‑stage journey—information discovery, focused problem solving, and escalation—and proposes design guidelines and tailored product strategies, such as clearer UI, precise suggestions, and rapid human‑hand‑off, to improve satisfaction.

AIIntelligent Customer ServiceUser Research

0 likes · 7 min read

User Segmentation and Journey Mapping for Intelligent Customer Service: Insights for Positive and Negative Explorers