Smart model improves instant booking and overall booking rates
A Smart Instant Book Filter Model based on Dual-Objective Optimization
Artificial Intelligence
Summary
Booking short-term rentals online can be tricky because some listings require approval before confirming, which causes delays or rejections. The authors created a smart system that increases the number of listings that can be instantly booked without lowering the total number of successful bookings. Their approach uses a special optimization method to balance fast booking with maintaining quality. Tests with real users showed this smart system works better than previous manual tuning methods, and it is now used by millions every day.
What this means in practice
- •For marketplace platform engineers: Implement booking filters that increase instant bookings without reducing overall successful bookings for lodging marketplaces.
- •For travel app developers: Enhance user experience by reducing booking delays through optimized promotion of instant book listings in search results.
Authors
Yi Hou, Li Fan, Huizhong Duan
Abstract
Hospitality service in online marketplaces has become increasingly popular over the past few years, not only because it helps people easily rent short-term lodging but also because it empowers travelers to connect with locals and feel belonging. A typical pattern of these industries is a central platform mediating the economic interactions between guests and hosts, where a successful booking experience is the key to user satisfaction. However, one of the biggest challenges is the possible delay of response and chance of rejection when guests try to book a request-to-book listing. Previously, we enabled the instant book feature to allow hosts to instantly accept qualified guests and guests to confirm bookings immediately. Yet the symptom has not been alleviated given mixed request-to-book and instant book listing supply in search results. One possible solution is to promote all instant book listings in search results; however, booking rate will be damaged due to some improperly promoted low quality listings. In this paper, we present a novel smart instant book filter system that applies the instant book filter intelligently and shifts request-to-book to instant book without hurting booking rate. Compared with traditional two-sided markets research that targets finding strategies to maximize a single overall social welfare goal, our proposed system aims to maximize the instant book rate with the constraint that booking rate should not decrease, forming a dual-objective optimization problem. We show A/B testing results for our system exposed to global users, demonstrating that our model performs better than previous human-expert-tuned models in terms of both instant book rate and overall booking rate. We also achieved improved metrics such as the host instant book adoption rate. The proposed system has been deployed to production and is serving millions of users every day.