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

TL;DR

This paper introduces a machine learning-based quality rating system for vacation rentals, enhancing search and marketing by providing explainable ratings, validated through large-scale online experiments on Booking.com.

Contribution

It presents a novel, explainable quality rating system for vacation rentals, addressing the lack of official ratings and demonstrating successful deployment at scale.

Findings

System impacted over a million accommodations

Validated effectiveness through online controlled experiments

Enhanced guest decision-making and provider marketing

Abstract

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.