  Booking.com is a virtual two-sided marketplace where guests and accommodation
providers are the two distinct stakeholders. They meet to satisfy their
respective and different goals. Guests want to be able to choose accommodations
from a huge and diverse inventory, fast and reliably within their requirements
and constraints. Accommodation providers desire to reach a reliable and large
market that maximizes their revenue. Finding the best accommodation for the
guests, a problem typically addressed by the recommender systems community, and
finding the best audience for the accommodation providers, are key pieces of a
good platform. This work describes how Booking.com extends such approach,
enabling the guests themselves to find the best accommodation by helping them
to discover their needs and restrictions, what the market can actually offer,
reinforcing good decisions, discouraging bad ones, etc. turning the platform
into a decision process advisor, as opposed to a decision maker. Booking.com
implements this idea with hundreds of Machine Learned Models, all of them
validated through rigorous Randomized Controlled Experiments. We further
elaborate on model types, techniques, methodological issues and challenges that
we have faced.
