Learning to Rank for Maps at Airbnb

TL;DR

This paper presents a new ranking algorithm for Airbnb's map interface, improving user experience by adapting the mathematical foundations of how users interact with map-based search results.

Contribution

It introduces a revised mathematical framework for ranking listings specifically tailored for map interfaces, addressing limitations of previous list-based ranking assumptions.

Findings

Significant improvement in user experience metrics

Successful experimental validation of the new ranking approach

Unified theory for list and map search interfaces

Abstract

As a two-sided marketplace, Airbnb brings together hosts who own listings for rent with prospective guests from around the globe. Results from a guest's search for listings are displayed primarily through two interfaces: (1) as a list of rectangular cards that contain on them the listing image, price, rating, and other details, referred to as list-results (2) as oval pins on a map showing the listing price, called map-results. Both these interfaces, since their inception, have used the same ranking algorithm that orders listings by their booking probabilities and selects the top listings for display. But some of the basic assumptions underlying ranking, built for a world where search results are presented as lists, simply break down for maps. This paper describes how we rebuilt ranking for maps by revising the mathematical foundations of how users interact with search results. Our iterative and experiment-driven approach led us through a path full of twists and turns, ending in a unified theory for the two interfaces. Our journey shows how assumptions taken for granted when designing machine learning algorithms may not apply equally across all user interfaces, and how they can be adapted. The net impact was one of the largest improvements in user experience for Airbnb which we discuss as a series of experimental validations.