
Applying deep learning to Airbnb search - godelmachine
https://blog.acolyer.org/2019/10/09/applying-deep-learning-to-airbnb-search/
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cleansy
Slightly OT: I hope that effort helps with their search results big time.
Because it's a pain. I was just looking for an apartment for some vacation and
cool that it showed 300+ results but a totally random ordering in my opinion.

I can't _sort_ by price. The first couple of results weren't even close to the
stuff I booked in the past. Plus even though I never booked anything but
accommodation with them I still have to select manually that I want
accommendation. Every single time, it's not even funny anymore. There are many
many UX flaws when I book with them that this actually drove me to more
traditional hotel comparison sites.

EDIT: removed some stronger language, but booking with Airbnb was great at one
point and it degraded greatly IMHO. If anyone from Airbnb reads this, I am
willing to get in contact directly. The feedback form is wasted time, I tried.
;-)

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rakefire
_but booking with Airbnb was great at one point and it degraded greatly IMHO._

I couldn't agree more. I'm traveling pretty often but in the last year I have
to say that I ended up hating Airbnb for their high fees, lack of customer
support,and the plenty flaws in their system. Lately, I'm using booking.com
just because are more professional and they even offer discounts after various
bookings. A big minus with booking would be the small hotel rooms with no
kitchen (not so cozy).

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kaybe
booking.com has always worked very well for me in terms of service, but the
user interface is extremely hostile to me with its nagging pop-ups and
stressful text in general.

I know they're trying to get me to book by showing me an imagined or real
shortage, but it stresses me out to the point I try to avoid them.

~~~
pxtail
After reading your description I took a moment to analyze my thoughts and
feelings toward booking and I think that first and easily distinguishable
emotion that came to my mind was mild anxiety.

I'm pretty sure that this hostile UI brings them money and that's why they are
doing it but on the other hand I'm wondering if in the long run customers
negative feelings/connotations toward service will eventually matter.

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zihotki
The article is about the progress of implementing deep learning, it mentiones
a few pitfalls and points of interest and that's it, no conclusions, no
interesting insides.

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IlegCowcat
It's a really honest description of the attempts to move to neural networks.

At least the way it's written it feels like no 'data scientists' were
involved, it was all done by data engineers (software developer rather than
statistical/modelling knowledge)... Which is depressing, if even Airbnb are
biased to hiring only good developers (rather than a mix)

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PaulRobinson
Most companies don't need to hire data scientists.

They need to hire engineers who can take an algorithm implemented well by
somebody else, and apply it to a business domain.

That's the future of deep learning, machine learning, and all other
linear/logistic regression style technologies.

The mathematicians are going to have to wait for the age of quantum annealing
to feel valuable again: reducing code into a function that works on a quantum
setup actually needs those skills that developers struggle with.

Everything else though, outside of pure research and in the vast majority of
companies, is already well on the road to commoditisation.

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mlthoughts2018
This is pure comedy. Want to waste a bunch of cloud resources operationalizing
a garbage model? Have engineers do it.

The most successful workflow strategy I’ve seen in practice is where the deep
learning researcher is also the person operationalizing the model. The same
person who is grokking the latest paper in arxiv is also studying correlations
in product data to perform feature engineering and also writing Dockerfiles to
make the work reproducible and optimizing containers for production
deployment, latency, failure tolerance, and evaluating performance in the
specific context of the business application and creating well crafted
software components with adequate testing along the way.

The commodity part is the cloud engineering, kubernetes pod setup, load
testing tools, and general software engineering. Machine learning engineers
are typically great at these things and they are easy to learn.

Meanwhile, learning about the nuance of hyperparameter tuning, how to evaluate
overfitting, model complexity tradeoffs, when to use which kind of statistical
modeling tool, how to improve models based on observing error cases, and a
host of other statistical modeling concerns are wildly not commoditizable at
this point of history. Knowing how to copy paste some Keras tutorials will not
help you.

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PaulRobinson
So I'll accept you might be right, except:

80% of real business problems are going to be solved by figuring out how to
get XGBoost working with it. And you're done.

Research is valuable, and there are some problems where doing real thinking is
useful, but pareto principle is at play here: a lot of people just aren't
going to need to do that, for the same reason most developers don't need to
know the difference between a merge sort and a quick sort: sorting was
commoditised into most programming languages decades ago. Same deal, different
tech with machine learning.

This is not a bad thing, and there will be a bumpy road, and there will always
be a market for experts to help with the edge cases, but most firms will drop
them like hot bricks within a decade.

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undefined3840
Airbnb search is terrible. I also can’t understand why they don’t let you save
filters e.g. if I’m always searching for an entire place with 1 bedroom then
let me save and apply that filter to every search.

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lammalamma25
You can't sort by price in search. I think that needs to be repeated anytime
someone talks about airbnb's search, website, etc.

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mannanj
yeah it's a pretty bad and unintuitive (and in my mind, manipulative) feature
reduction that pushes me away from Airbnb.

