GetYourGuide is a global online marketplace that helps travelers discover and book the best experiences. One of our core challenges is ensuring users always see the most relevant activities first—a task historically powered by an XGBoost-based ranking system. However, as we continued refining our tree-based models, returns on incremental improvements began to plateau. To spark our next step change in performance, we decided to adopt Deep Learning.

In this talk, we will share how, in just nine months, we migrated our ranking pipeline to a Deep Learning architecture while maintaining tight latency and high-throughput requirements. We will walk through our phased approach, starting with a minimal viable model to confirm our production setup and gradually increasing its complexity. Along the way, we tested over 50 iterations offline and ran more than 10 live A/B tests to validate the impact on our customers. Ultimately, we rolled out a PyTorch transformer-based model with significant business impact. We will also discuss the main challenges we faced on the operational and modeling sides, how we overcame them, and the lessons we learned.

You will leave with practical strategies for transitioning from traditional tree-based models to neural networks in production. Join us to learn how to advance your machine-learning capabilities and unlock new dimensions of relevance and personalization for real-time ranking.