# Improving Deep Learning For Airbnb Search

arXiv: https://arxiv.org/abs/2002.05515 · HTML: https://mlsanity.com/paper/2002.05515/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 19 of 19)

- [Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://mlsanity.com/paper/2212.13937/) (2022)
- [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)
- [Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank](https://mlsanity.com/paper/2309.15560/) (2023)
- [Measurement and applications of position bias in a marketplace search engine](https://mlsanity.com/paper/2206.11720/) (2022)
- [Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending](https://mlsanity.com/paper/2408.09168/) (2024)
- [BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale](https://mlsanity.com/paper/2410.02126/) (2024)
- [Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples](https://mlsanity.com/paper/2509.03187/) (2025)
- [A General Framework for Debiasing in CTR Prediction](https://mlsanity.com/paper/2112.02767/) (2021)
- [Learning Monotonic Probabilities with a Generative Cost Model](https://mlsanity.com/paper/2506.03542/) (2025)
- [GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou](https://mlsanity.com/paper/2507.15267/) (2025)
- [Applying Embedding-Based Retrieval to Airbnb Search](https://mlsanity.com/paper/2601.06873/) (2026)
- [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)
- [CLAX: Fast and Flexible Neural Click Models in JAX](https://mlsanity.com/paper/2511.03620/) (2025)
- [High Precision Audience Expansion via Extreme Classification in a Two-Sided Marketplace](https://mlsanity.com/paper/2602.14358/) (2026)
- [Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb](https://mlsanity.com/paper/2607.12246/) (2026)
- [Improving Item Discoverability in e-Commerce Search via Related Intent Generation](https://mlsanity.com/paper/2607.27172/) (2026)
- [SIFT: Search Intent-to-Filter Transformer for Multi-Task Personalized Filter Ranking at Airbnb](https://mlsanity.com/paper/2610.07810/) (2026)

## Abstract (arXiv)

The application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next after you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, B for bias and C for cold start. For architecture, we describe a new ranking neural network, focusing on the process that evolved our existing DNN beyond a fully connected two layer network. On handling positional bias in ranking, we describe a novel approach that led to one of the most significant improvements in tackling inventory that the DNN historically found challenging. To solve cold start, we describe our perspective on the problem and changes we made to improve the treatment of new listings on the platform. We hope ranking teams transitioning to deep learning will find this a practical case study of how to iterate on DNNs.