# Learning To Rank Diversely At Airbnb

arXiv: https://arxiv.org/abs/2210.07774 · HTML: https://mlsanity.com/paper/2210.07774/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [Learning a Deep Listwise Context Model for Ranking Refinement](https://mlsanity.com/paper/1804.05936/) (2018)
- [SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval](https://mlsanity.com/paper/1912.05891/) (2019)
- [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)

## Built on by (most cited first, 6 of 6)

- [Inference-time Stochastic Ranking with Risk Control](https://mlsanity.com/paper/2306.07188/) (2023)
- [Learning to Rank for Maps at Airbnb](https://mlsanity.com/paper/2407.00091/) (2024)
- [Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning](https://mlsanity.com/paper/2408.13399/) (2024)
- [High Precision Audience Expansion via Extreme Classification in a Two-Sided Marketplace](https://mlsanity.com/paper/2602.14358/) (2026)
- [JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling](https://mlsanity.com/paper/2606.19108/) (2026)
- [RMB: Reward Model Boosting Mitigates Reward Hacking](https://mlsanity.com/paper/2609.33221/) (2026)

## Abstract (arXiv)

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.