Here’s the thing about AI: It’s pretty much the only tech breakthrough in the past decade, maybe even longer, that demonstrates touchable, tasteable, real-life, concrete, measurable ROI.
And the measurable impact that machine learning has had on Airbnb’s unique technological challenge — creating great matches between guests and hosts — has been “profound,” says the company’s VP of engineering Mike Curtis, who’s a featured speaker at MB 2017 coming up July 11-12.
Airbnb connects millions of guests searching for the right place to stay and millions of hosts offering distinct spaces. Airbnb’s unique technological challenge is to personalize each match between guest and host.
The goal is to create a “great match between a guest and a host that’s going to lead to a great experience out there in the real world,” says Curtis.
Helping guests find the perfect place
A big part of the magic lies in personalizing rank search results for guests coming to the site.
Initially, search rankings were determined by a set of hard-coded rules based on very basic signals, such as the number of bedrooms and price. And because they were hard coded, the rules were applied to every guest uniformly, rather than taking into account the unique values that could create the kind of a personalized experience that keeps guests coming back.
Airbnb learned over time that machine learning could be used to offer this personalization, Curtis said. Airbnb introduced its machine learned search ranking model toward the end of 2014 and has been continuously developing it since. Today Airbnb personalizes all search results.
Airbnb factors in signals about the guests themselves, as well as guests similar to them, when offering up results.
For example, guests provide explicit signals in their search — the length of stay, the number of bedrooms they need. But as they examine their search results, they may show interest in similar, desirable attributes that the guests themselves might not even notice.
“There’s a bunch of other signals that you’re giving us based on just which listings you click on,” Curtis says. “For example, what kind of setting is it in? What kind…
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