Machine Learning Times
Machine Learning Times
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2 years ago
Building Airbnb Categories with ML and Human-in-the-Loop

Originally published in The Airbnb Tech Blog, Nov 21, 2022. 


25 Years of Online Travel Search

Online travel search hasn’t changed much in the last 25 years. The traveler enters her destination, dates, and the number of guests into a search interface, which dutifully returns a list of options that best meet the criteria. Eventually, Airbnb and other travel sites made improvements to allow for better filtering, ranking, personalization and, more recently, to display results slightly outside of the specified search parameters–for example, by accommodating flexible dates or by suggesting nearby locations. Taking a page from the travel agency model, these websites also built more “inspirational” browsing experiences that recommend popular destinations, showcasing these destinations with captivating imagery and inventory (think digital “catalog”).

The biggest shortcoming of these approaches is that the traveler must have a specific destination in mind.  Even travelers who are flexible get funneled to a similar set of well-known destinations, reinforcing the cycle of mass tourism.

Introducing Airbnb Categories

In our recent release, we flipped the travel search experience on its head by having the inventory dictate the destinations, not the other way around. In this way, we sought to inspire the traveler to book unique stays in places they might not think to search for. By leading with our unique places to stay, grouped together into cohesive “categories”, we inspired our guests to find some incredible places to stay off the beaten path.

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13 thoughts on “Building Airbnb Categories with ML and Human-in-the-Loop

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