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From Sean Owen <sro...@gmail.com>
Subject Re: Reg. Netflix Prize Apache Mahout GSoC Application (SVD option)
Date Mon, 05 Apr 2010 21:22:19 GMT
Your audience is the project committers.

I wouldn't spend much time rehashing the SVD theory. You should name
your approach and I suppose write enough to make it clear you
understand the algorithm enough to implement it. In this case you can
assume we all understand the SVD well enough already.

I think the proposal should rather focus on how you'd structure the
implementation in the project, what the major classes are, and that
you've thought reasonably about how long it'll take to build, test,
and document.

And to reiterate what Jake said, yeah there is already a fairly clear
structure for Hadoop jobs in the recommender area -- you'd want to
subclass AbstractJob and run series of Mappers/Reducers. You're going
to emulate that for sure. You can probably reuse many of the Writable
representations, in that package and in math or common, to represent
the intermediate outputs.

What's really key to your proposal is sketching the series of
map-reduces that you'd drop into this structure to get the job done.


On Mon, Apr 5, 2010 at 9:38 PM, Richard Simon Just
<info@richardsimonjust.co.uk> wrote:
> I was wondering when it comes to the proposal what sort of background
> detail should I be going into? Should I be talking about the use of SVD
> within a recommender situation for example? Or given that the Mentors
> already know this should I be discussing purely what sort of SVD-based
> recommender implementation I'm planning? I guess a question  beside the
> question is am I aiming the proposal to people who are familiar with
> Mahout and Machine Learning or to other people as well?

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