# Learning to Rank for Maps at Airbnb

arXiv: https://arxiv.org/abs/2407.00091 · HTML: https://mlsanity.com/paper/2407.00091/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://mlsanity.com/paper/1804.05938/) (2018)
- [Smooth Loss Functions for Deep Top-k Classification](https://mlsanity.com/paper/1802.07595/) (2018)
- [Applying Deep Learning To Airbnb Search](https://mlsanity.com/paper/1810.09591/) (2018)
- [Improving Deep Learning For Airbnb Search](https://mlsanity.com/paper/2002.05515/) (2020)
- [Managing Diversity in Airbnb Search](https://mlsanity.com/paper/2004.02621/) (2020)
- [Unbiased Learning to Rank: Online or Offline?](https://mlsanity.com/paper/2004.13574/) (2020)
- [Optimizing Airbnb Search Journey with Multi-task Learning](https://mlsanity.com/paper/2305.18431/) (2023)
- [Learning To Rank Diversely At Airbnb](https://mlsanity.com/paper/2210.07774/) (2022)

## Abstract (arXiv)

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.