Applying Embedding-Based Retrieval to Airbnb Search
Mustafa Abdool, Soumyadip Banerjee, Moutupsi Paul, Do-kyum Kim, Xioawei Liu, Bin Xu, Tracy Yu, Hui Gao, Karen Ouyang, Huiji Gao, Liwei He, Stephanie Moyerman, Sanjeev Katariya

TL;DR
This paper describes the development and deployment of an embedding-based retrieval system for Airbnb search, addressing unique challenges in a dynamic marketplace, and demonstrating significant improvements in key performance metrics.
Contribution
The paper introduces a novel embedding-based retrieval system tailored for Airbnb's complex marketplace, including modeling insights, evaluation methods, and online deployment strategies.
Findings
Significant improvement in booking conversion rates
Successful deployment in multiple search use-cases
Effective handling of dynamic inventory and user preferences
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…
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Taxonomy
TopicsSharing Economy and Platforms · Recommender Systems and Techniques · Information Retrieval and Search Behavior
