# Personalizing Airbnb search by learning from the guest journey

DevFeed: [Personalizing Airbnb search by learning from the guest journey](<https://devfeed.tech/articles/personalizing-airbnb-search-by-learning-from-the-guest-journey-1219.md>)

Original publisher: [Read original article](<https://medium.com/airbnb-engineering/personalizing-airbnb-search-by-learning-from-the-guest-journey-bcefd1915624?source=rss----53c7c27702d5---4>)

Author: Daochen Zha

Published: 2026-07-21T17:01:04Z

Content type: article

Language: en

Sources: [The Airbnb Tech Blog - Medium](<https://devfeed.tech/sources/the-airbnb-tech-blog-medium.md>)

Topics: [Transformer](<https://devfeed.tech/topics/transformer.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Temporal data](<https://devfeed.tech/topics/temporal-data.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [blog](<https://devfeed.tech/tags/blog.md>), [conversion](<https://devfeed.tech/tags/conversion.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [research](<https://devfeed.tech/tags/research.md>), [scale](<https://devfeed.tech/tags/scale.md>), [search](<https://devfeed.tech/tags/search.md>), [technology](<https://devfeed.tech/tags/technology.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

Airbnb describes a Transformer-based sequence model for personalizing search by encoding years of guest behavior, including listing views, bookings, reviews, and cancellations. The system learns richer representations of guest preferences to improve listing relevance and booking conversion while addressing very long, noisy event sequences and the cost of training on hundreds of millions of search-label pairs.

## Source excerpt

How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie Moyerman, and Sanjeev Katariya Introduction Planning a trip on Airbnb rarely happens in a single session. A guest searching for a place to stay in San Francisco might browse dozens of listings over several days, leaving behind a trail of views. Typically, over a period of years, that same guest will have accumulated many previous bookings, reviews, and the occasional cancellation. Taken together, these events reveal a great deal about what that guest values in a stay. For years, Airbnb's search ranking captured this through hand-crafted features: aggregated statistics such as total past bookings or average listing price. These worked well, but as the feature count grew into the hundreds, the approach became harder to scale and increasingly limited in expressiveness. In this blog post, we describe how we built a sequence modeling system that encodes the full guest journey using a Transformer, learning richer representations of guest preferences to deliver more personalized search results. An example of a guest journey, which is typically long, exploratory, and complex.Challenges Event sequences per guest present three core challenges. First, they are dominated by listing views, which account for the vast majority of all events -- some guests accumulate hundreds of thousands of them -- making raw sequences computationally intractable to model directly. The distribution of event types, with the majority being listing views. Second, unlike social media platforms, which optimize for engagement, Airbnb optimizes for booking conversion. Bookings are rare, compared to events, and deliberate, whereas a listing view could reflect genuine intent or simply idle browsing. Building a model that generalizes