cs.AIOct 26, 2026

A Smart Instant Book Filter Model based on Dual-Objective Optimization

Authors: Yi Hou, Li Fan, Huizhong Duan

Organizations: Instant Book, Airbnb Inc, San Francisco, California · Search Ranking, Airbnb Inc, San Francisco, California

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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.

Figures & tables

Figure 1. Graph representation of booking entities and relationship
Figure 2. One complete learning episode
Figure 3. Smart Instant Book filter system workflow
ParametersInstant Bookings GainBookings Gain
Human expert picked parameter set 111%-0.90%
Human expert picked parameter set 23.50%-0.80%
Human expert picked parameter set 326%-1.60%
Human expert picked parameter set 421%-1.60%
Human expert picked parameter set 515%-1.30%
Human expert picked parameter set 66.10%-0.17%
Table 1. Experiment results comparing our learned parameters with human expert picked parameters
Figure 4. Smart instant book filter with its tooltip
Figure 5. Contextual market insight reminder card
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