分析用户预订行为，优化民宿平台匹配与定价工具。
Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example
- 结合经济建模与因果推断，分析价格等因子对预订的影响。
- 发现用户对价格敏感度存在差异，影响匹配与定价策略。
- 适合平台算法优化、市场机制设计相关研究者参考。
Airbnb 是基于连接与归属感的社区，许多房东是普通人，通过分享生活空间为客人营造家的感觉。平台致力于连接人与地方。为实现这一目标，我们提供工具帮助房东设定有竞争力的价格，提升客人的可负担性并增加房源预订量；同时个性化推荐符合客人需求的房源。为支持这些举措，我们结合经济建模与因果推断方法，研究客人在不同价格及其他因素影响下的预订行为，并识别其在不同客人和房源间的偏好异质性。该理解有助于发现平台改进机会，例如优化房东定价工具，使其更精准平衡供需；或根据客人对价格及其他因素的响应程度，实现更个性化的匹配，从而提升整体体验。
原文摘要 · Abstract (English)
Airbnb is a community based on connection and belonging -- many hosts on Airbnb are everyday people who share their worlds to provide guests with the feeling of connection and being at home; Airbnb strives to connect people and places. Among our efforts to connect guests and hosts, we provide tools to enable hosts to set competitive prices, which helps improve affordability for guests while helping hosts get more bookings. We also personalize the guest experience to show them the listings that match their needs. To help inform these efforts, we combine economic modeling and causal inference techniques to understand how guests book stays based on the prices hosts set, among other factors, and how that preference varies across different guests and listings. Such understanding helps us identify opportunities for Airbnb to support the marketplace and better connect guests and hosts. For example, understanding how much guests respond to different prices helps optimize the tools that we provide to hosts, in order to enable hosts to choose and set competitive prices that further balance demand and supply. As another example, understanding heterogeneity in guest preferences helps us personalize the guest experience and better match them with the listings that meet their needs, based on how much they respond to different prices and other factors.
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