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From Jayesh <jayesh.sidhw...@gmail.com>
Subject Re: Using Mahout for low-volume data
Date Mon, 15 Jul 2013 05:49:11 GMT
Hi Ted,

Thanks for the reply.
My training data could have around 100k users and around 1k items. The data
is sparse (I have a boolean affinity - the user either bought the item or
did not)

PS: I have been playing around with a sample code, using Loglikelihood
Similarity to get a 24% precision, is this a par score?



On Mon, Jul 15, 2013 at 10:58 AM, Ted Dunning <ted.dunning@gmail.com> wrote:

> Mahout will work fine for smaller data sizes.
>
> Collaborative filtering can be difficult in general with small data,
> however.
>
> How many users and how many items?  How many actions?
>
>
> On Sun, Jul 14, 2013 at 10:22 PM, Jayesh <jayesh.sidhwani@gmail.com>
> wrote:
>
> > Hello,
> >
> > I am exploring the collaborative filtering algorithms in Mahout to build
> a
> > recommendation engine.
> >
> > I had recently gone for a Big Data conference where the speakers
> suggested
> > that using Mahout is overkill for anything that doesn't have some
> terabytes
> > of training data.
> >
> > I tried to google some cases on that, but no help, so turned to this
> > thread.
> >
> > Would you suggest me using Mahout in my case when the training data might
> > not be in terabytes, but some gigabytes, perhaps few 100s of megabytes?
> > If not, do you suggest any other approach?
> >
> > Thank you.
> >
> > --
> > Best Regards,
> >
> > Jayesh
> >
>



-- 
Best Regards,

Jayesh

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