Applying Embedding-Based Retrieval to Airbnb Search

2026-01-11

arXiv.org

AI Summary

This work proposes an embedding-based retrieval (EBR) system to address the challenge of efficient search in Airbnb’s dynamic marketplace, characterized by vast and evolving inventory, heterogeneous user preferences, and multi-product scenarios such as flexible-date searches. The approach leverages deep learning models to generate semantic embeddings for both queries and listings, enabling low-latency candidate retrieval via approximate nearest neighbor search. Innovatively, the system captures key characteristics of a two-sided marketplace—including dynamic inventory availability, long conversion funnels, and diverse retrieval contexts—and integrates seamlessly into a multi-stage ranking architecture. Upon deployment, the EBR system significantly improved booking conversion rates and has been successfully applied across core business scenarios, including standard search, flexible-date search, and personalized marketing emails.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

The goal of Airbnb search is to match guests with the ideal accommodation that fits their travel needs. This is a challenging problem, as popular search locations can have around a hundred thousand available homes, and guests themselves have a wide variety of preferences. Furthermore, the launch of new product features, such as \textit{flexible date search,} significantly increased the number of eligible homes per search query. As such, there is a need for a sophisticated retrieval system which can provide high-quality candidates with low latency in a way that integrates with the overall ranking stack. This paper details our journey to build an efficient and high-quality retrieval system for Airbnb search. We describe the key unique challenges we encountered when implementing an Embedding-Based Retrieval (EBR) system for a two sided marketplace like Airbnb -- such as the dynamic nature of the inventory, a lengthy user funnel with multiple stages, and a variety of product surfaces. We cover unique insights when modeling the retrieval problem, how to build robust evaluation systems, and design choices for online serving. The EBR system was launched to production and powers several use-cases such as regular search, flexible date and promotional emails for marketing campaigns. The system demonstrated statistically-significant improvements in key metrics, such as booking conversion, via A/B testing.

Problem

• Embedding-Based Retrieval
• Airbnb Search
• Two-sided Marketplace
• Large-scale Retrieval
• Dynamic Inventory

Innovation

• Embedding-Based Retrieval
• Two-sided Marketplace
• Dynamic Inventory
• Flexible Date Search
• Retrieval-Ranking Integration