Post by High Priestess on Oct 5, 2015 3:22:17 GMT
Peter shared in SEpt 2015
Airbnb Shares The Keys To Its Infrastructure
"'Everything that we do in engineering is about creating great matches between people,' says Curtis. 'Every traveler and every host is unique, and people have different preferences for what they want out of a travel experience. So a lot of the work that we do in engineering is about how do we match the right people together for a real world, offline experience. It is part of everything we do. Part of it is machine learning, part of it is search ranking, part of it is fraud detection and getting bad people off of the site and verifying people’s identity so they are who they say they are. Part of it is about the user interface and how we get explicit signals about your preferences. A lot of the technology that we do is around creating great matches.'
The trick is to use search engines and machine learning to bubble up the best options for both guest and host based on their preferences, which are either explicit in profiles or implicit in the history of transactions and searches on the Airbnb site. So, for example, say you want to go to Paris – the one in France, not Texas – for a few days a few weeks from now. Paris has about 40,000 listings on Airbnb, and maybe 10,000 of those are available. Based on your preferences, there might be something like 1,000 possible places for you to stay. It is just not possible to click through those 1,000 places, and the number remains large after you put on filters for various kinds of preferences – WiFi, no pets, a whole house not a room, and so forth. The idea that Airbnb is developing is to augment search with machine learning such that the five or ten best matches for both guests and hosts will pop up, speeding up the transaction time, which makes people happier and which also reduces frustration and loading on Airbnb’s systems."
"That deep understanding of guests and hosts also drives a feature that Airbnb introduced about a year ago, called Instant Book, which as the name suggests allows for guests to book a stay instantly. But Curtis says that this requires a deep understanding of host preferences and guest behavior to do, it is not just a matter of running a credit card (which would be easy enough). About a year ago, when it started, about 40,000 Airbnb sites had Instant Booking, and now it is at about 200,000 of the 1.2 million sites worldwide."
Click here bit.ly/1XQm9ih for ThePlatform article.
My two cents: This is based on an interview with Mike Curtis, Airbnb's VP of Engineering. Much of it is in tech-speak for computer engineering nerds, but there's quite a bit of interesting information about the future direction of Airbnb with machine learning and yes, our old friend Instant Book. A long article, but worth the read. Tip: just skip the paragraph once it begins to sound to geeky.
Keith:
I'm a big fan of Instant Book.. it's only done me any good.
the machine learning (formerly called "Artificial Intelligence"
) leaves much to be desired at AirBnb, but they'll get better.
The problem with such a thing is that machines need data from which to learn... since most people travel infrequently, there isn't sufficient data to do good personality based matching. also, the way places are listed doesn't make it easy to understand what type of person would best fit a specific listing.
eventually there will be a way to profile a listing on a personality scale then match that more precisely to an individual. .
it'll be exciting to watch... especially as more personality types start using airbnb for their travels.
Airbnb Shares The Keys To Its Infrastructure
"'Everything that we do in engineering is about creating great matches between people,' says Curtis. 'Every traveler and every host is unique, and people have different preferences for what they want out of a travel experience. So a lot of the work that we do in engineering is about how do we match the right people together for a real world, offline experience. It is part of everything we do. Part of it is machine learning, part of it is search ranking, part of it is fraud detection and getting bad people off of the site and verifying people’s identity so they are who they say they are. Part of it is about the user interface and how we get explicit signals about your preferences. A lot of the technology that we do is around creating great matches.'
The trick is to use search engines and machine learning to bubble up the best options for both guest and host based on their preferences, which are either explicit in profiles or implicit in the history of transactions and searches on the Airbnb site. So, for example, say you want to go to Paris – the one in France, not Texas – for a few days a few weeks from now. Paris has about 40,000 listings on Airbnb, and maybe 10,000 of those are available. Based on your preferences, there might be something like 1,000 possible places for you to stay. It is just not possible to click through those 1,000 places, and the number remains large after you put on filters for various kinds of preferences – WiFi, no pets, a whole house not a room, and so forth. The idea that Airbnb is developing is to augment search with machine learning such that the five or ten best matches for both guests and hosts will pop up, speeding up the transaction time, which makes people happier and which also reduces frustration and loading on Airbnb’s systems."
"That deep understanding of guests and hosts also drives a feature that Airbnb introduced about a year ago, called Instant Book, which as the name suggests allows for guests to book a stay instantly. But Curtis says that this requires a deep understanding of host preferences and guest behavior to do, it is not just a matter of running a credit card (which would be easy enough). About a year ago, when it started, about 40,000 Airbnb sites had Instant Booking, and now it is at about 200,000 of the 1.2 million sites worldwide."
Click here bit.ly/1XQm9ih for ThePlatform article.
My two cents: This is based on an interview with Mike Curtis, Airbnb's VP of Engineering. Much of it is in tech-speak for computer engineering nerds, but there's quite a bit of interesting information about the future direction of Airbnb with machine learning and yes, our old friend Instant Book. A long article, but worth the read. Tip: just skip the paragraph once it begins to sound to geeky.
Keith:
I'm a big fan of Instant Book.. it's only done me any good.
the machine learning (formerly called "Artificial Intelligence"
) leaves much to be desired at AirBnb, but they'll get better. The problem with such a thing is that machines need data from which to learn... since most people travel infrequently, there isn't sufficient data to do good personality based matching. also, the way places are listed doesn't make it easy to understand what type of person would best fit a specific listing.
eventually there will be a way to profile a listing on a personality scale then match that more precisely to an individual. .
it'll be exciting to watch... especially as more personality types start using airbnb for their travels.