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Learning to Match TSMO_2018_paper_10.pdf - Google Drive
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 inven-
tory, fast and reliably within their requirements and constraints.

Accommodation providers desire to reach a reliable and large mar-
ket 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 accom-
modation 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 actu-
ally 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 rigor-
ous Randomized Controlled Experiments. We further elaborate on

model types, techniques, methodological issues and challenges that
we have faced.
ML  guide  rails  response  distribution  analysis  booking.com 
march 2018 by foodbaby

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