
Giving Away Our Recommendation Engine - kky
http://blog.mortardata.com/post/82195614895/giving-away-our-recommendation-engine-for-free
======
mck-
Fwiw, here are two light-weight feature-based recommendation engines I built
for Node.js (for situations where you have the cold-start problem and
therefore can't rely on user/item based collaborative filtering): Alike [1]
and Look-Alike [2]

[1] [https://github.com/axiomzen/Alike](https://github.com/axiomzen/Alike)

[2] [https://github.com/axiomzen/Look-Alike](https://github.com/axiomzen/Look-
Alike)

~~~
yblu
Thanks for sharing. What do you mean by the "cold-start" problem? Just want to
know exactly when I can use your engines.

~~~
elwell
Just speculating: not having a recommendation when you first begin because you
don't have any data.

~~~
mck-
Exactly right. I borrowed that term from Chapter 2 on Collective Intelligence
[1]

[1]
[http://shop.oreilly.com/product/9780596529321.do](http://shop.oreilly.com/product/9780596529321.do)

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contingencies
So hang on, what exactly is a recommendation engine?

They give examples of LinkedIn ( _people you may know_ ) and Amazon
(presumably _other people who bought this_ , _so-and-so 's list of such-a-
subject books_).

That makes sense, though the segment of businesses that may actually benefit
seems limited. Social stuff, sure. Most of us? What's the minimum
recommendable-entity/category-or-user threshold that this makes sense for? Is
success with these sorts of engines merely a reflector of poor UI design in
your normal UX? (Of the above examples, the first seems very unidimensional -
in that it's basically a simple graph distance - and the latter also rather
rudimentary and often irrelevant).

So what exactly is this thing providing? Graph analysis? I think not. It reads
more like some kind of raw timestamped user behavioural event data processing
to infer relationships between users or products they interact with. Reading
through the docs it seems this is a layer on top of Apache Pig
([https://pig.apache.org/](https://pig.apache.org/)) - _a high-level language
for expressing data analysis programs, coupled with infrastructure for
evaluating these programs_. I think clarity in explaining this thing could be
improved, particularly selling clearly what a recommendation is and when its
useful. Using phrases like "award winning" doesn't help.

PS. Why all the downvotes? Sheesh.

~~~
icambron
I suspect you're being downvoted for having a dismissive tone in the same
breath as you admit to not understanding the problem space. My guess is that
the marketing copy on the site isn't targeted towards you, so it shouldn't
surprise you that you don't understand it, whereas their target customers know
all sorts of things about, say, Pig. That's fine, but then your comment should
read like, "Can someone explain to me what this is for?" Instead, your comment
is dripping with condescending snark, lecturing someone or another about how
this thing you don't understand probably isn't useful, and incredulous that
you don't understand something on the internet.

Imagine opening an advanced textbook on a subject you don't understand,
reading two paragraphs of it, and throwing up your hands in disgust because
_what does this even mean?_

~~~
contingencies
Apologies, condescending snark was not the intent (I can't even see where you
see that, actually!). In response to your more salient points, it could be
argued that a web+EC2 layer on top of existing software is hardly an _advanced
textbook_. Likewise, their announcement's stated intent is to gain customers,
so feedback on what's unclear should be well within an acceptable scope of
discussion. Finally, I doubt any of us are excluded from their intended market
as software people move frequently between problem domains.

~~~
erichmond
For the record, this reply seems as snarky and dismissive as the first.
Hopefully some constructive criticism.

~~~
contingencies
Thanks.

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olidb2
FWIW we've been using the mortar platform to run large pig jobs without a fuss
at [http://datadog.com](http://datadog.com) and we've been very happy with it.
Glad to see them contribute their recommender code too.

~~~
alecsmart1
Can you please suggest why you need a recommendation engine for datadog?

~~~
olidb2
We don't use the recommendation engine but the underlying platform, which
makes it really simple to write and run pig jobs. Though the majority of our
business deals with real-time data processing, the ability to crunch numbers
in batch without dev or ops overhead is attractive and well worth the price to
us.

~~~
kldavenport
Is this better or similar to Hue?

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pixelmade
I'm curious what the business case was for open sourcing the code. Maybe to
create an ecosystem?

~~~
showerst
From the "What you'll need" section of the first tutorial -

A Mortar account. You can sign up for a free Public account with Mortar here.
If you want to keep your customized recommendation engine code private, you
will need a Solo-level account ($99/month). Beyond that, you'll only pay for
your actual usage of AWS cloud services (we never add an upcharge).

Kudos for the open source, but it looks like to actually use this for business
you'll still need to pay. Unless i'm misreading it, "Open source but you'll
still have to go through our platform" is pretty disingenuous.

~~~
ethanbond
It reads like "open source but not free to make proprietary." First, it's
awesome just to see source as something to learn from. Second, it seems
reasonable they don't want people forking, modifying then profiting from their
work without contributing back to it - either by also releasing source or by
paying.

I think it's a nice model actually.

~~~
catern
It's called the GPL. If that's really the model they're trying to create, it
would be nice if they just used the GPL.

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dsheth
Anyone know of any comparisons between this and Apache Mahout? I've used
Mahout's Item-Item recommender in the past, and it's worked well, just
wondering if there were advantages to this recommender.

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ASquare
I'm sure plenty of good karma (even the non-HN kind) is headed your way -
kudos.

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X4
WOW, Awesome Documentation and Product!! Kudos and Greetings from Germany 😊

Those who know what Hadoop, Pig and the whole "Data Science Stack" is, will
find this surely useful.

