AI News, How Airbnb uses Machine Learning to Detect Host Preferences

How Airbnb uses Machine Learning to Detect Host Preferences

At Airbnb we seek to match people who are looking for accommodation — guests — with those looking to rent out their place — hosts.

I remembered my friend’s behavior and was curious to discover what affects hosts’ decisions to accept accommodation requests and how Airbnb could increase acceptances and matches on the platform.

What started as a small research project resulted in the development of a machine learning model that learns our hosts’ preferences for accommodation requests based on their past behavior.

For each search query that a guest enters on Airbnb’s search engine, our model computes the likelihood that relevant hosts will want to accommodate the guest’s request.

If accepted and booked, a request may leave the host with a sub-window before the check-in date (check-in gap — April 5–7) and/or a sub-window after the check-out (check-out gap — April 10).

Indeed, when I plotted hosts’ tendency to accept over the sum of the check-in gap and the check-out gap (3+1= 4 in the example above), as in the next plot, I found the effect that I expected to see: hosts were more likely to accept requests that fit well in their calendar and minimize gap days.

The plot below looks at the dispersion of hosts’ preferences for last minute stays (less than 7 days) versus far in advance stays (more than 7 days).

Indeed, the dispersion in preferences reveals that some hosts like last minute stays better than far in advance stays — those in the bottom right — even though on average hosts prefer longer notice.

I found similar dispersion in hosts’ tendency to accept other trip characteristics like the number of guests, whether it is a weekend trip etc.

All these findings pointed to the same conclusion: if we could promote in our search results hosts who would be more likely to accept an accommodation request resulting from that search query, we would expect to see happier guests and hosts and more matches that turned into fun vacations (or productive business trips).

We set out to associate hosts’ prior acceptance and decline decisions by the following characteristics of the trip: check-in date, check-out date and number of guests.

At first glance, this seems like a perfect case for collaborative filtering — we have users (hosts) and items (trips) and we want to understand the preference for those items by combining historical ratings (accept/decline) with statistical learning from similar hosts.

As a method of imputation, we smoothed the preference using a weight function that, for each trip characteristic, averages the median preference of hosts in the region with the host’s preference.

To test the online performance of the model, we launched an experiment that used the predicted probability of host acceptance as a significant weight in our ranking algorithm that also includes many other features that capture guests’ preferences.

Every time a guest in the treatment group entered a search query, our model predicted the probability of acceptance for all relevant hosts and influenced the order in which listings were presented to the guest, ranking likelier matches higher.

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