
NeoWize (YC S16) Personalizes E-Commerce Sites to New Users - stvnchn
http://themacro.com/articles/2016/08/neowize/
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pravula
"For example, if a new user lands onto a site selling watches, we’ll have a
really expensive product and a relatively cheap product. If the user clicks on
the expensive product and ignores the other one, then we know they want
expensive watches and we’ll show higher-end products on the next page."

How is that deep learning?

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swalsh
I can imagine it being a bit more sophisticated than that, such as after a few
clicks they learn a preference for leather straps or gold faceplates, or a
classic design so you start sorting "browse" pages differently. If you have
enough data, you might also be able to discern "customers who looked at x
usually end up buying y". Those preferences could also be carried forward if
they're making use of retargeting ads.

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omernev
This is very accurate :-)

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aresant
Fascinating product, sorry for story formatting comment but I think you missed
an h4 for "You’re gathering a lot of information on user behavior. Have you
noticed any trends?"

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stvnchn
Good catch - thanks!

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Ben2863
Could you please elaborate a bit regarding how much logical integration is
needed between different sites? I mean, how much tweaking does the algorithm
need (if at all) in order to fit a specific site design or a specific product?

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omernev
I hope I'm understanding the question correctly. If I don't answer fully, go
ahead and comment again...

Mostly, the answer is that the algorithm doesn't need tweaking between
different sites. It's even a bit more generic than that: It mostly needs
possible actions to take (in our case - products it can show), information
sources (in our case - indications of interest), and a goal function to
maximize (in our case - something like revenue).

We're playing around a bit with slightly different weights to different
verticals - but definitely now for specific stores.

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omernev
Hi friends! Omer, co-founder of NeoWize, here. I'll be checking in here for
the next day or so and would love to answer any questions you may have...

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ikeboy
Doesn't say what kind of integration is required. The site mentions a plugin
but not what system it's a plugin for.

If I have a custom site, what are the chances you could work with it without
expensive customization?

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omernev
Good question! Integration is one of those incredibly important points that
usually isn't "exciting" enough to get a lot of attention. But it's actually a
huge factor in our business.

We have 3 ways to integrate with stores:

1\. Plugin for supported platforms: Shopify, Magento and AbanteCart for now.
This method is definitely the simplest one - usually not much more than a one
click install.

2\. API calls: We open APIs for reporting information (which user showed
interest in which product) and decision (which items should I show this user
on this page). This will usually result in a very small amount of lines of
code inserted at various places in the store's codebase. It's also great
because it really is quite independent of any specifics of the store's
implementation.

3\. Custom JS: This will result in a small but not negligible amount of work
on our side which will result in the store needing to insert a single line of
JS into their template and everything will work. We'll generally only do this
with larger stores where we can justify the development on our end.

In any case, we don't want the store owner to have to do any meaningful amount
of work in order for our product to run.

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zanalyzer
What's the difference between neural networks and deep learning? In my
understanding deep learning involves simply multi-layered neural networks.

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omernev
People don't always use concepts with the exact same definitions in mind. I
sometimes interchange between deep learning and neural networks without
thinking about it (even though I probably shouldn't).

Generally speaking, neural networks is a very specific type of algorithm. It's
been around since the 80s, with the most interesting changes since then being
faster computers and more data.

Usually, when I hear people say deep learning, they mean one of three thing:
Sometimes they mean more specific types of deep (= more layers) neural
networks, sometimes a more general class of algorithms (out of which NN are
probably the most important by far) or sometimes they're using a meaningless
buzzword because it's trending...

I know it's uncool, but the Wikipedia article on Deep Learning is actually
quite a useful read to get a sense of the jargon and what everything means...

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j-m-h
how do your methods compare to the standard active learning approaches in the
academic literature e.g.
[http://people.stern.nyu.edu/ddzyabur/index_files/Active_Mach...](http://people.stern.nyu.edu/ddzyabur/index_files/Active_Machine_Learning_for_Consideration_Heuristics.pdf)

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omernev
I remember reading this article. We do a few things differently, though we try
to take what we can from existing literature:

1\. They actively ask questions. We use the natural product placement in
stores as our "questions". This means a lot of consideration for whether a
user has even seen this product and page placement.

2\. Another consideration that arises from using natural product placement is
that we don't have a "stopping rule". We always need to balance the need to
convert into a sale on this page with learning for the next one.

3\. One more thins is: We absolutely need to use what they call "complex
decision heuristics". Even what they describe there as examples of complex
heuristics are sometimes insufficient to describe user behavior in our data.

TLDR: Most of the basic concepts are shared (which probably makes sense), but
our usage means a lot of small things are different and some of them are quite
interesting research projects in themselves...

