Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example
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Computer Science > Machine Learning
Title:Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example
Abstract: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.
| Comments: | 5 pages, 3 figures. Presented at the KDD 2024 Workshop on Two-Sided Marketplace Optimization, Barcelona, Spain |
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY); Econometrics (econ.EM); Applications (stat.AP) |
| Cite as: | arXiv:2607.00280 [cs.LG] |
| (or arXiv:2607.00280v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00280
arXiv-issued DOI via DataCite (pending registration)
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