Recommending Accommodation Filters with Online Learning
Presented in the Workshop on Online Recommender Systems and User Modeling ORSUM ACM RecSys 2020 by Lucas Bernardi, Pablo Estevez, Matias Eidis and Eqbal Osama
Abstract
Online Accommodations Platforms match guests searching for accommodation with hospitality service providers. A fundamental characteristic of efficient platforms is the ability to satisfy the needs and preferences of the guests. To achieve this goal, a common search tool is the Results Filtering capability which allows users to refine query results with explicit criteria. However, as supply grows and diversifies, more filtering options become available, reaching hundreds of different criteria for one query, and making it hard for customers to find the ones that are relevant to them. In this work we present the implementation of an Accommodation Filters Recommender System addressing this issue. The problem poses several challenges around recommendations feedback, user experience constraints, and non stationarity among others. We provide an end-to-end description of the System, discuss implementation issues and provide techniques to address them including a large scale distributed online learning architecture. The solution was validated through several Online Controlled Experiments performed in Booking.com, a top Online Travel Agency serving millions of daily users, showing statistically significant results on various user behaviour metrics indicating a strong positive effect on User Engagement.
