Mining the Stars: Learning Quality Ratings with User-facing Explanations for Vacation Rentals

Online Travel Platforms are virtual two-sided marketplaces where guests search for accommodation and accommodation providers list their properties including hotels and vacation rentals among others. 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. This is relevant for the business because it directly impacts the number of transactions in 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 rental suppliers to market their product ef-fectively. In this work we describe a machine learned quality rating system for vacation rentals. The problem is challenging mainly due to the lack of ground truth and explainability requirements. We present techniques to address such 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.