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Giving Away Our Recommendation Engine

blog.mortardata.com

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Re: Giving Away Our Recommendation Engine

#31

Post by sandGorgon »

gmisra wrote: post #17
showerst wrote: post #11

Earlier quoted context omitted.

I'm not trying to start a flame war over the use of the word open, and I think it's great that they're releasing code that others can learn from. It's just that making a big press release and blog post that brags about open sourcing, vs the reality that you can't actually do anything substantial with the code without paying for it... it seems off to me. I get what they're trying to do, but to me the whole point of OS…

Thanks for the clarification. I'm in agreement with your opinions regarding false promises of open source, and also that this is increasingly a problem. However, I don't think that actually applies here. Specifics: 1. Everything in this github repository ( https://github.com/mortardata/mortar-recsys ) appears to be truly open - it's just a bunch of pig scripts, some java UDF definitions, and some python management co…

I cant find the part which provisions the mapreduce cluster - isnt that part of their platform lockin ?

Re: Giving Away Our Recommendation Engine

#32

Post by dllthomas »

rjurney wrote: post #26
dllthomas wrote: post #22

Earlier quoted context omitted.

To my mind it depends a little on what functionality is behind the account. If this is a huge chunk of functionality, that presently depends on their infrastructure but doesn't have to, then I think that's fine (though certainly enough to self-host would be better). If this is basically a thin wrapper and all the actual functionality is in their proprietary server code, then it's hugely disingenuous.

The code does not depend on the infrastructure. You can execute the Pig code locally on your machine, or on your own hadoop cluster. This really is a 'free' give-away of code.

Fantastic! Thanks.

Re: Giving Away Our Recommendation Engine

#33

Post by contingencies »

erichmond wrote: post #27
contingencies wrote: post #24

Earlier quoted context omitted.

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…

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

Thanks.

Re: Giving Away Our Recommendation Engine

#34

Post by NyxWulf »

sandGorgon wrote: post #31
gmisra wrote: post #17

Earlier quoted context omitted.

Thanks for the clarification. I'm in agreement with your opinions regarding false promises of open source, and also that this is increasingly a problem. However, I don't think that actually applies here. Specifics: 1. Everything in this github repository ( https://github.com/mortardata/mortar-recsys ) appears to be truly open - it's just a bunch of pig scripts, some java UDF definitions, and some python management co…

I cant find the part which provisions the mapreduce cluster - isnt that part of their platform lockin ?

You can use Amazon Elastic MapReduce to provision your own cluster. I'm guessing the value prop they bring is the ease of handling that part for you.

Re: Giving Away Our Recommendation Engine

#35

Post by yblu »

mck- wrote: post #23

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

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

Re: Giving Away Our Recommendation Engine

#36

Post by elwell »

yblu wrote: post #35
mck- wrote: post #23

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

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

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

Re: Giving Away Our Recommendation Engine

#37

Post by stonemetal »

contingencies wrote: post #10

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 f…

> benefit seems limited. Social stuff, sure. Most of us?

Any business where you have a large catalog that users are going to want to filter through. This gives you the ability to offer a shortcut to things they might find interesting. Other examples would be netflix, spotify, app stores, or coursera.

Re: Giving Away Our Recommendation Engine

#39

Post by mck- »

elwell wrote: post #36
yblu wrote: post #35

Earlier quoted context omitted.

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

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

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

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

Re: Giving Away Our Recommendation Engine

#40

Post by jhorey »

sandGorgon wrote: post #31
gmisra wrote: post #17

Earlier quoted context omitted.

Thanks for the clarification. I'm in agreement with your opinions regarding false promises of open source, and also that this is increasingly a problem. However, I don't think that actually applies here. Specifics: 1. Everything in this github repository ( https://github.com/mortardata/mortar-recsys ) appears to be truly open - it's just a bunch of pig scripts, some java UDF definitions, and some python management co…

I cant find the part which provisions the mapreduce cluster - isnt that part of their platform lockin ?

If you want a relatively simple way to provision a Hadoop cluster locally, you may want to try out Ferry (http://ferry.opencore.io). It's based off Docker, so in theory, you could also write a nice Dockerfile to deploy Mortar's recommendation engine. (full disclosure, I'm the author of Ferry).
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