Applying Embedding-Based Retrieval to Airbnb Search
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908757318959104 |
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| author | Abdool, Mustafa Banerjee, Soumyadip Paul, Moutupsi Kim, Do-kyum Liu, Xioawei Xu, Bin Yu, Tracy Gao, Hui Ouyang, Karen Gao, Huiji He, Liwei Moyerman, Stephanie Katariya, Sanjeev |
| author_facet | Abdool, Mustafa Banerjee, Soumyadip Paul, Moutupsi Kim, Do-kyum Liu, Xioawei Xu, Bin Yu, Tracy Gao, Hui Ouyang, Karen Gao, Huiji He, Liwei Moyerman, Stephanie Katariya, Sanjeev |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06873 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Applying Embedding-Based Retrieval to Airbnb Search Abdool, Mustafa Banerjee, Soumyadip Paul, Moutupsi Kim, Do-kyum Liu, Xioawei Xu, Bin Yu, Tracy Gao, Hui Ouyang, Karen Gao, Huiji He, Liwei Moyerman, Stephanie Katariya, Sanjeev Information Retrieval Machine Learning 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. |
| title | Applying Embedding-Based Retrieval to Airbnb Search |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2601.06873 |