Keynote 1

Fast, Flexible and Personalized: Leveraging Bandits for Travel Recommendations

by Andrea Marchini (Expedia Group)


Personalised travel suggestions lead to better engagement but require sufficient user history. Multi-armed bandits overcome this through on-the-fly learning. This presentation will demonstrate how online multi-armed bandit algorithms can optimise suggestions by efficiently learning from user responses. We will explain key online bandit concepts and algorithms like Thompson sampling. Real-world examples will showcase bandit applications for travel including dynamic image optimization contextual content ranking and banner text optimization.
Attendees will discover how online bandits enable rapid personalisation without historical data by dynamically adapting recommendations based on user feedback. The talk will provide strategies for implementing bandits to tailor travel recommendations amidst limited user data. Rapid adaptation to feedback enables bandits to balance exploration of new options and exploitation of the best ones.

About the speaker

Andrea Marchini is a Senior Machine Learning Scientist specializing in reinforcement learning and contextual bandits. As the Science Lead of the Reinforcement Learning team at Expedia Group, he plays a central role in developing AI services to optimize real-time customer experiences using contextual bandits. With over 9 years of experience, Andrea has successfully applied machine learning techniques to drive impact across various industries including online travel, food delivery and automotive. His expertise encompasses both theoretical foundations in machine learning and implementing scalable production ML systems. He holds a Ph.D. in Physics, where he developed expertise in areas like Bayesian inference. Passionate about continuing to unlock the potential of artificial intelligence, Andrea is always eager to exchange ideas and discuss emerging innovations in the field.

Keynote 2

Scaling and Standardising ML experimentation for Ranking

by Kostia Kofman (


During his time at, Kostia Kofman worked on various aspects of recommendation systems, from the algorithmic to the applicative aspects. For the last two years, he led the search results ranking ML group, focusing on the largest scale ML system within

About the speaker

In this talk, Kostia will share their journey towards a modernized ML solution for search results ranking. He will unveil some of the technical building blocks essential for supporting progress and evolution in large-scale problems. Additionally, he will discuss the modeling approaches that were developed based on the infrastructure and tools they enabled—modeling approaches that led to a significant increase in business metrics.