基于双目标优化的智能 instant book 房源过滤模型
A Smart Instant Book Filter Model based on Dual-Objective Optimization
AI 导读
论文提出一种智能 instant book 过滤系统，在搜索结果中把 request-to-book 房源智能转化为 instant book，同时以预订率不下降为约束最大化 instant book 率，构成双目标优化问题。全球用户的 A/B 测试显示，其表现优于此前人工专家调优的模型，预订率和房东 instant book 采纳率均提升。该系统已上线生产，每天服务数百万用户。
正文 · 原文
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.
| Comments: | 6 pages, 5 figures. Presented at TSMO'18: WSDM 2018 Workshop on Two-sided Marketplace Optimization: Search, Pricing, Matching & Growth, Marina Del Rey, CA, USA |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36287 [cs.AI] |
| (or arXiv:2610.36287v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36287 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yi Hou [view email]
[v1]
Mon, 26 Oct 2026 21:11:30 UTC (683 KB)
来源：arXiv cs.AI · arxiv.org