  Online Travel Platforms are virtual two-sided marketplaces where guests
search for accommodations and accommodation providers list their properties
such as hotels and vacation rentals. The large majority of hotels are rated by
official institutions with a number of stars indicating the quality of service
they provide. It is a simple and effective mechanism that contributes to match
supply with demand by helping guests to find options meeting their criteria and
accommodation suppliers to market their product to the right segment directly
impacting the number of transactions on the platform. Unfortunately, no similar
rating system exists for the large majority of vacation rentals, making it
difficult for guests to search and compare options and hard for vacation
rentals suppliers to market their product effectively. In this work we describe
a machine learned quality rating system for vacation rentals. The problem is
challenging, mainly due to explainability requirements and the lack of ground
truth. We present techniques to address these challenges and empirical evidence
of their efficacy. Our system was successfully deployed and validated through
Online Controlled Experiments performed in Booking. com, a large Online Travel
Platform, and running for more than one year, impacting more than a million
accommodations and millions of guests.
