Booking.com Data Science

The Data Science and Machine learning blog of Booking.com

Editors
Dima Goldenberg

Machine Learning @ Booking.com

Sonya Liberman

Leading applied Machine Learning teams. Especially enjoy bringing theory to production and bridging the gap between business needs and technological solutions.

Maud Schwoerer

Data Scientist at Booking.com

Felipe Moraes

Machine Learning Scientist at Booking.com. PhD in Information Retrieval at TU Delft. Ex-Amazon Alexa.

Latest Posts

Highlights of Booking.com publications in 2024

At Booking.com, our mission is to make experiencing the world easier for everyone. We are committed to investing in cutting-edge technology…

Tune Smarter, Not Harder: A Deep-Dive into Hyperparameter Tuning in Payment Fraud at Booking.com

Exploring how we boosted model performance and reduced training time in our payment fraud model by improving hyperparameter optimization

Booking.com’s publications at the 18th ACM conference on Recommender Systems

Booking.com’s mission is to make it easier for everyone to experience the world. We invest in technology that helps take the friction out…

Predicting cancellations with survival modeling

Within Booking.com, we have a long tradition of predicting reservation cancellation probability using machine learning models. Historically…

Meta-experiments: Improving experimentation through experimentation

At Booking.com, we are proud of our data-driven culture. A hallmark of this is our extensive use of A/B testing everywhere in the company…

Raising the bar by lowering the bound

Why tackling overestimation is essential for product development

How good are your ML best practices?

by  George Chouliaras, Kornel Kielczewski, Amit Beka, David Konopnicki and Lucas Bernardi

Booking.com @ KDD 2023

Booking.com contributions at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining at Long Beach, CA, USA

Sequential Testing at Booking.com

Reliable and fast product decisions

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