71–80 of 93 posts
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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"?
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I believe booking.com ran on perl for a very long time. Maybe still does. ~relevant quotes from https://github.com/globalcitizen/taoup ... In #devops is turtle all way down but at bottom is perl script. - @devops_borat Comedy: You, trying to launch a startup from scratch using Java. Tragedy: Me, trying to debug 27k lines of legacy Perl that brings $113MM/yr - @NeckbeardHacker
It still does. Booking and ZipRecruiter are easily the two largest employers of Perl programmers.
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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"?
Lifetime value (LTV). It’s tricky to measure in the travel industry because users only transact once every few months or even years.
But that leaves you at the mercy of the competition, which in an open data business (I mean, airlines are more than happy to tell you which flights they have available) implies that your product is undifferentiated. So eventually you resort to this tactics: as soon as a user gets in, do anything that's humanly possible to convert that sucker.
That's in essence what's wrong with Booking. This trickles down, unfortunately: Ryanair hides most of its costs in effectively forcing you to upgrade to Premium in order to be treated as something just a bit more than lifestock, because the assumption that travelers' main concern is price pervades the industry, even if the price they are shown isn't the price they pay in the end.
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Coming from the math side, I don't really get this. Isn't all of the latency introduced by the learning side of things? Shouldn't the answer side be entirely decoupled from the learning, and simply be plugging data into an equation with a bunch of constant parameters (with values discovered by your learning system, updated at a less than realtime frequency)?
The "equation with a bunch of constant parameters" generated by an ML model can be huge, with thousands of inputs or more. Evaluating that equation for a specific observation can require a huge number of computations, which is why there's a boom in ML inference hardware right now.
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?
And people like me and you, who really don't want to book at Booking.com and make an effort to book elsewhere, are often in bad luck, because they own a bunch of other booking sites too.
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This. I still (grudgingly) use them because they also seem to have figured out that its important that your booking process works and it's friction-free, and they often do have the best price. But if I find an alternative that has the same width of offers and a booking process that doesn't feel like a drill sergeant constantly yelling "BOOK NOW YOU WORTHLESS SCUM, BOOK, BOOK, WHAT ARE YOU WAITING FOR YOU IMBECILE, CL…
> what do they think will happen? Booking.com A/B tests everything : the drill-sergeant-like funnel probably has higher conversion rates than any gentler variation. So to answer your question - they think you might not book through them without the shoutiness.
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This. I still (grudgingly) use them because they also seem to have figured out that its important that your booking process works and it's friction-free, and they often do have the best price. But if I find an alternative that has the same width of offers and a booking process that doesn't feel like a drill sergeant constantly yelling "BOOK NOW YOU WORTHLESS SCUM, BOOK, BOOK, WHAT ARE YOU WAITING FOR YOU IMBECILE, CL…
> what do they think will happen? Booking.com A/B tests everything : the drill-sergeant-like funnel probably has higher conversion rates than any gentler variation. So to answer your question - they think you might not book through them without the shoutiness.
>> 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…
For the same hardware load, you can process several samples instead of just one.
Pre-computation means running your model on samples in advance, before the model result is needed, so it's ready to use instantly when needed.
Caching works probably because there are model results that are reused again and again, so it makes sense to cache them. For example, there are deep models that process room pictures, room and customer characteristics. Only customer characteristics change between customers, so it makes sense to cache the features output by the deep CNN that processes the room pictures.
Once you start doing prediction at scale, there are lots of these optimizations to pick up.
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The "equation with a bunch of constant parameters" generated by an ML model can be huge, with thousands of inputs or more. Evaluating that equation for a specific observation can require a huge number of computations, which is why there's a boom in ML inference hardware right now.
Yeah, I don't buy that. It's still just a matrix multiplication (for the linear bits). Incredibly fast. Besides, the old physics rule of thumb is that any real world equation with a bunch of parameters only has 5-7 that actually matter, and only 3 that matter a lot. Everything else can be set to zero without noticeable change in the result.
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Yeah, I don't buy that. It's still just a matrix multiplication (for the linear bits). Incredibly fast. Besides, the old physics rule of thumb is that any real world equation with a bunch of parameters only has 5-7 that actually matter, and only 3 that matter a lot. Everything else can be set to zero without noticeable change in the result.
If you’re making decisions that involve multiple variables you may be doing hundreds to thousands of inferences for a single page load. Keeping latency under 50ms becomes a real challenge.
x1 * a + x2 * b + x3 * c + ... + x1000 * zzz + ...
If a, b, c ... zzz, are all fixed constants already discovered by your learning algorithm. That's a very fast calculation, and doesn't take anything like 50ms.
Also, in the real world, you can establish a significance cutoff for a lot of these constants and get something like this as your final equation:
x13 * m + x523 * cdf + x777 * wdc + x893 * ydz