[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/) - 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.

[alexhawdon]

https://www.coursera.org/course/recsys will provide you with a good introduction to the topic

[contingencies]

Thanks. For others who are interested, that course apparently uses a different piece of software called LensKit http://lenskit.grouplens.org/

Could anyone summarize the difference between Pig and LensKit when applied to recommendation systems?

[rjurney]

The mortar recommendation system is written in Pig, and can scale to any data size, even petabytes. The lenskit tool cannot.

[stonemetal]

> 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.

[contingencies]

Basically those are all media discovery applications. (PS. I can't think of a single app store experience worth replicating...)

[stonemetal]

Drop the media part and yeah sounds about right. Anything that has a large enough collection of things to need discovery.