# Personalizing Airbnb Search by Learning from the Guest Journey

Using Transformer-based models to understand guest preferences and personalize search.

- **Source:** [The Airbnb Tech Blog - Medium](https://brifing.si/source/ce2bec98-ea5a-48cd-9fed-71a3f27d04c8)
- **Published:** 2026-07-21
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://medium.com/airbnb-engineering/personalizing-airbnb-search-by-learning-from-the-guest-journey-bcefd1915624?source=rss----53c7c27702d5---4>
- **On Brifing:** https://brifing.si/s/003391bc-90be-49f6-97f6-15f792e1c306

## Introduction

Planning a trip on Airbnb rarely happens in a single session. A guest searching for a place in San Francisco might browse dozens of listings over days, leaving a trail of views. These behaviors reveal what the guest values, but processing this data with hand-crafted features was challenging and limited.

## Challenges

Working with guest journey data presents three core challenges: data volume, sparse booking signals, and computational cost. Detecting meaningful bookings among millions of views and processing them efficiently is critical for the model's success.

## Solution: Two-Part Sequence

To tackle these challenges, we split the guest sequence into two parts: the long-term sequence includes bookings and reviews from the past seven years, while the short-term sequence captures views from the last 21 days. This approach provides both depth and recency.

## Efficient Training

To speed up model training, we used three strategies: batching searches, bucketing sequences by length, and sparse computation. These methods improved training efficiency fourfold and reduced costs.

## Serving Design

To ensure low latency in real-time searches, we processed sequences in daily batches and stored the results. When a guest searches, the model retrieves this stored data to deliver fast, personalized results.

## Advantages of Transformer Model

The Transformer-based model can represent guest preferences more richly. Unlike traditional methods, it handles complex sequences better and provides more accurate personalization.

## A/B Testing

Before launching the new model, we conducted three-week A/B tests. We monitored key business metrics and guardrails to evaluate the model's performance.

## Results

The new model helped guests find suitable listings faster. Personalized search increased booking rates and improved user experience.

## Significance

This work highlights the importance of processing large-scale behavioral data and delivering personalized experiences. For platforms like Airbnb, it's a critical step to enhance guest satisfaction and business performance.

## Sources covering this story

- https://medium.com/airbnb-engineering/personalizing-airbnb-search-by-learning-from-the-guest-journey-bcefd1915624?source=rss----53c7c27702d5---4 — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._