The company uses a machine-learned search ranking model to personalize results for guests. The model factors in guests’ tendencies to click on certain bookings. For example, Airbnb might look at whether customers favor specific types of décor in places they book. The company feeds more than 100 characteristics into the model, which then uses the data to identify patterns and personalize search rankings.
It also uses host preferences to personalize search results for guests, promoting hosts likely to accept the accommodation request.
In addition, Airbnb uses ML in its predictive pricing model to help hosts price their listings. The model uses historical travel patterns for the area to find the likelihood of any listing being booked at any time.
Optimizing matches between hosts and guests will be critical to Airbnb’s success as it continues to grow.
