High Precision Audience Expansion via Extreme Classification in a Two-Sided Marketplace

TL;DR

This paper presents a novel high-precision geographic retrieval method for Airbnb search, using extreme classification to efficiently identify relevant map cells, improving the upstream filtering process before ranking.

Contribution

It introduces a new approach to location retrieval in marketplace search by rearchitecting the system to use extreme classification over 25 million map cells, enhancing precision.

Findings

Improved retrieval precision reduces irrelevant listings.

Enhanced filtering efficiency speeds up the search process.

System demonstrates scalability over a large geographic dataset.

Abstract

Airbnb search must balance a worldwide, highly varied supply of homes with guests whose location, amenity, style, and price expectations differ widely. Meeting those expectations hinges on an efficient retrieval stage that surfaces only the listings a guest might realistically book, before resource intensive ranking models are applied to determine the best results. Unlike many recommendation engines, our system faces a distinctive challenge, location retrieval, that sits upstream of ranking and determines which geographic areas are queried in order to filter inventory to a candidate set. The preexisting approach employs a deep bayesian bandit based system to predict a rectangular retrieval bounds area that can be used for filtering. The purpose of this paper is to demonstrate the methodology, challenges, and impact of rearchitecting search to retrieve from the subset of most bookable high precision rectangular map cells defined by dividing the world into 25M uniform cells.