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?
Successful machine learning models: lessons learned at Booking.com
61–70 of 93 posts
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.
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
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…
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
#65Earlier 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…
Re: Successful machine learning models: lessons learned at Booking.com
#66Earlier 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…
Re: Successful machine learning models: lessons learned at Booking.com
#67Earlier 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."
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.
Re: Successful machine learning models: lessons learned at Booking.com
#69Re: Successful machine learning models: lessons learned at Booking.com
#70Nice 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?