# Applying Embedding-Based Retrieval to Airbnb Search

arXiv: https://arxiv.org/abs/2601.06873 · HTML: https://mlsanity.com/paper/2601.06873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [Embedding-based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2106.09297/) (2021)
- [Applying Deep Learning To Airbnb Search](https://mlsanity.com/paper/1810.09591/) (2018)
- [TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval](https://mlsanity.com/paper/2002.06275/) (2020)
- [Improving Deep Learning For Airbnb Search](https://mlsanity.com/paper/2002.05515/) (2020)
- [OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search](https://mlsanity.com/paper/2404.16260/) (2024)
- [Managing Diversity in Airbnb Search](https://mlsanity.com/paper/2004.02621/) (2020)
- [Optimizing Airbnb Search Journey with Multi-task Learning](https://mlsanity.com/paper/2305.18431/) (2023)

## Built on by (most cited first, 1 of 1)

- [Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan](https://mlsanity.com/paper/2602.06654/) (2026)

## Abstract (arXiv)

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 textitflexible 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.