AirBnB personalises, tunes search results
Airbnb learned over time that machine learning could be used to offer this personalization, Mike 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 of decor is in the house? These are things Airbnb can use to feed into the model to come up with a better prediction of which listings to show you first."
The company pulls well over a hundred signals into the search rank model, Curtis says, and then the machine learning algorithm figures out how all the signals interact, to produce personalized search rankings