Abstract

Booking.com is the world's largest online travel agent. Millions of guests find their accommodation, and millions of accommodation providers list their properties, including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years, we have applied Machine Learning to improve our customers' experience and our business. While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We analyzed about 150 successful customer-facing machine learning applications, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide, and validated through rigorous randomized controlled trials. Following the phases of a machine learning project, we describe our approach, the many challenges we found, and the lessons we learned while scaling up such a complex technology across our organization. Our main conclusion is that an iterative, hypothesis-driven process integrated with other disciplines was fundamental to building 150 successful products enabled by machine learning.

Pablo Estevez Castillo

Principal Data Scientist @ Booking.com

Pablo Estevez is Principal Data Scientist at Booking.com. He has worked on recommendations, personalization, and experimentation across the Booking.com website and manages several machine learning, data science, and product development teams.