# Personalizing Airbnb search by learning from the guest journey

[Airbnb](https://yomu.fyi/company/airbnb) · Daochen Zha · Jul 21, 2026

## Summary

Airbnb replaced hundreds of hand-crafted ranking features with a Transformer-based sequence model that captures both long-term booking history and short-term browsing behavior. To keep latency low and throughput high, sequence representations are generated via daily batch jobs and combined with real-time queries using a co-trained setwise ranker.

## Takeaways

- Guest histories are split into long-term events (up to 7 years of bookings and cancellations, capped at 80 events) and short-term events (past 21 days of listing views, capped at 200 events).
- Training throughput improved fourfold by using causally masked search batching to share a single forward pass across multiple searches, sequence-length bucketizing, and sparse calculations.
- Serving latency is minimized by precomputing sequence embeddings in daily batch jobs and retrieving them in real time for a setwise ranker that scores listing candidate sets together.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Performance](https://yomu.fyi/topic/performance), [Recommendation Systems](https://yomu.fyi/topic/recommendation-systems), [Search](https://yomu.fyi/topic/search)

[Read original post](https://medium.com/airbnb-engineering/personalizing-airbnb-search-by-learning-from-the-guest-journey-bcefd1915624)