# Applying Deep Learning To Airbnb Search

arXiv: https://arxiv.org/abs/1810.09591 · HTML: https://mlsanity.com/paper/1810.09591/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 24 of 24)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [LiRank: Industrial Large Scale Ranking Models at LinkedIn](https://mlsanity.com/paper/2402.06859/) (2024)
- [EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer](https://mlsanity.com/paper/2109.04235/) (2021)
- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [Optimizing Airbnb Search Journey with Multi-task Learning](https://mlsanity.com/paper/2305.18431/) (2023)
- [Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint](https://mlsanity.com/paper/2007.12349/) (2020)
- [Learning To Rank Diversely At Airbnb](https://mlsanity.com/paper/2210.07774/) (2022)
- [MicroNN: An On-device Disk-resident Updatable Vector Database](https://mlsanity.com/paper/2504.05573/) (2025)
- [Muddling Label Regularization: Deep Learning for Tabular Datasets](https://mlsanity.com/paper/2106.04462/) (2021)
- [On the Generalizability and Predictability of Recommender Systems](https://mlsanity.com/paper/2206.11886/) (2022)
- [ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale](https://mlsanity.com/paper/2105.08275/) (2021)
- [With One Voice: Composing a Travel Voice Assistant from Re-purposed Models](https://mlsanity.com/paper/2108.11463/) (2021)
- [Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey](https://mlsanity.com/paper/2308.08234/) (2023)
- [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)
- [On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga](https://mlsanity.com/paper/2408.06799/) (2024)
- [Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning](https://mlsanity.com/paper/2408.13399/) (2024)
- [Scale-Invariant Learning-to-Rank](https://mlsanity.com/paper/2410.01959/) (2024)
- [From Features to Transformers: Redefining Ranking for Scalable Impact](https://mlsanity.com/paper/2502.03417/) (2025)
- [Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank](https://mlsanity.com/paper/2507.20753/) (2025)
- [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)

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!