How Airbnb Tells You Will Enjoy Sunset Sailing in Barcelona? Recommendation in a Two-Sided Travel Marketplace

Abstract

A two-sided travel marketplace is an E-Commerce platform where users can both host tours or activities and book them as a guest. When a new guest visits the platform, given tens of thousands of available listings, a natural question is that what kind of activities or trips are the best fit. In order to answer the question, a recommender system needs to both understand characteristics of its inventories, and to know the preferences of each individual guest. In this work, we present our efforts on building a recommender system for Airbnb Experiences, a two-sided online marketplace for tours and activities. Traditional recommender systems rely on abundant user-listing interactions. Airbnb Experiences is an emerging business where many listings and guests are new to the platform. Instead of passively waiting for data to accumulate, we propose novel approaches to identify key features of a listing and estimate guest preference with limited data availability. In particular, we focus on extending the knowledge graph and utilizing location features. We extend the original knowledge graph to include more city-specific concepts, which enables us to better characterize inventories. In addition, since many users are new to the business, and the limited information of cold-start guests are categorical features, such as locations and destinations, we propose to utilize categorical information by employing additive submodels. Extensive experiments have been conducted and the results show the superiority of the proposed methods over state-of-the-art approaches. Results from an online A/B test prove that the deployment of the categorical feature handling method leads to statistically significant growth of conversions and revenue, which concludes to be the most influential experiment in lifting the revenue of Airbnb Experiences in 2019.