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Successful machine learning models: lessons learned at Booking.com

blog.acolyer.org

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Re: Successful machine learning models: lessons learned at Booking.com

#61
Nice to see that I am not only one hating booking.com with a passion.

But what really amazes me is the market failure that hotels and other accomodation providers can't come up with a co-op booking site. I am sure there are issues that are difficult to solve from competition point of view, but are they really so difficult to solve that the rent seeking fees of current booking sites are justified?

Re: Successful machine learning models: lessons learned at Booking.com

#62

"Content Overload: Accommodations have very rich content, e.g.descriptions, pictures, reviews and ratings." Laughed at that one. Booking.com is so full of dark patterns that I dread using it.

Booking is obsessed with maximizing conversions, which just leads to dark patterns. One lessons from all these things is they maximize for what’s easiest to measure, not what’s most important. Conversions aren’t the end all be all, nobody wants to come back to a store with the pushy salesperson.

If everything in this post and comment threads are true, I'm not sure what good it does to post on HN.

I bet a "How to dox and stalk people with Python" post would be flagged down, so maybe I'm just complaining about the prevailing ethics on the site.

Re: Successful machine learning models: lessons learned at Booking.com

#63

"Content Overload: Accommodations have very rich content, e.g.descriptions, pictures, reviews and ratings." Laughed at that one. Booking.com is so full of dark patterns that I dread using it.

What do you mean by "dark patterns"? I'm not familiar with that term

Google's right over there.

Re: Successful machine learning models: lessons learned at Booking.com

#64

>> Booking.com go to some lengths to minimise the latency introduced by models, including horizontally scaled distributed copies of models, a in-house developed custom linear prediction engine, favouring models with fewer parameters, batching requests, and pre-computation and/or caching. Any idea what these are ? especially the pre-computation/caching and batching. I'm not able to see what advantage does batching bri…

Here is an overly simple example:

Pre-compute the recommended hotels for my top users every night. Now when that user comes back, they see a slightly stale recommendation, but it's lightning fast.

You can also pre-compute and cache some of the inputs cheer model, like maybe a vector representation of the description of a hotel.

Re: Successful machine learning models: lessons learned at Booking.com

#65

Earlier quoted context omitted.

What do you mean by "dark patterns"? I'm not familiar with that term

Deceiving, tricking and pressuring users into taking actions. For example, LinkedIn having a flow that has an e-mail and password box, which will get a less attentive user to just re-enter their LinkedIn credentials. But it's actually a phishing form for your e-mail, so if your LinkedIn and e-mail password is the same, you have now "consented" to have your address book scraped and your contacts spammed. Or, in the ca…

Reviews have changed now and you can leave an overall rating (at least somewhere, they may be A/B testing). The reaction from Hosts has been negative. They should read your comment as they fail to understand the logic behind the change.

Re: Successful machine learning models: lessons learned at Booking.com

#66

Earlier quoted context omitted.

What do you mean by "dark patterns"? I'm not familiar with that term

Deceiving, tricking and pressuring users into taking actions. For example, LinkedIn having a flow that has an e-mail and password box, which will get a less attentive user to just re-enter their LinkedIn credentials. But it's actually a phishing form for your e-mail, so if your LinkedIn and e-mail password is the same, you have now "consented" to have your address book scraped and your contacts spammed. Or, in the ca…

For a while, somebody (not me) in the infrastructure department was maintaining a greasemonkey (I know) script that would remove the urgency messaging elements from the site. They used it both for themselves and to make a point about how much more pleasant the site was without them.

Re: Successful machine learning models: lessons learned at Booking.com

#67
post #52

Earlier quoted context omitted.

Booking is obsessed with maximizing conversions, which just leads to dark patterns. One lessons from all these things is they maximize for what’s easiest to measure, not what’s most important. Conversions aren’t the end all be all, nobody wants to come back to a store with the pushy salesperson.

I'll get the phrasing wrong here but "What is easy to measure will be deemed important what is difficult to measure will be deemed unimportant."

Oh, absolutely! How can you measure "I'm sick of being tricked into buying", "I wish this site treated me with respect", "I'm the client, and I feel like the product"?

Re: Successful machine learning models: lessons learned at Booking.com

#68

"Content Overload: Accommodations have very rich content, e.g.descriptions, pictures, reviews and ratings." Laughed at that one. Booking.com is so full of dark patterns that I dread using it.

i just want to note that "dark patterns" appear because it could be just one more A/B test that booking.com obsessively implements again and again. If the tested pattern does not help to increase conversion rate, it will be shut down. what's wrong with that?

Re: Successful machine learning models: lessons learned at Booking.com

#70

Nice to see that I am not only one hating booking.com with a passion. But what really amazes me is the market failure that hotels and other accomodation providers can't come up with a co-op booking site. I am sure there are issues that are difficult to solve from competition point of view, but are they really so difficult to solve that the rent seeking fees of current booking sites are justified?

Yes it is. You are maybe not considering the challenge of putting all those accommodations across the globe in one place. Accommodation providers don’t care about how they fill up their rooms as long as they get filled. Booking, contrary to many other tech companies, is very successful financially with 14.5b of revenues and something like 25+% EBIT. I find hard to believe that a coop is going to be able to build such service worldwide.
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