# Optimizing Airbnb Search Journey with Multi-task Learning

arXiv: https://arxiv.org/abs/2305.18431 · HTML: https://mlsanity.com/paper/2305.18431/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 10 of 10)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [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)
- [Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back](https://mlsanity.com/paper/1803.04062/) (2018)

## Built on by (most cited first, 7 of 7)

- [TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences](https://mlsanity.com/paper/2409.12972/) (2024)
- [Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking](https://mlsanity.com/paper/2508.00751/) (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)
- [BiListing: Modality Alignment for Listings](https://mlsanity.com/paper/2508.20396/) (2025)
- [JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling](https://mlsanity.com/paper/2606.19108/) (2026)

## Abstract (arXiv)

At Airbnb, an online marketplace for stays and experiences, guests often spend weeks exploring and comparing multiple items before making a final reservation request. Each reservation request may then potentially be rejected or cancelled by the host prior to check-in. The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking. In this paper, we present Journey Ranker, a new multi-task deep learning model architecture that addresses these challenges. Journey Ranker leverages intermediate guest actions as milestones, both positive and negative, to better progress the guest towards a successful booking. It also uses contextual information such as guest state and search query to balance guest and host preferences. Its modular and extensible design, consisting of four modules with clear separation of concerns, allows for easy application to use cases beyond the Airbnb search ranking context. We conducted offline and online testing of the Journey Ranker and successfully deployed it in production to four different Airbnb products with significant business metrics improvements.