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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Using Mahout for low-volume data
Date Mon, 15 Jul 2013 15:35:10 GMT
With such small data, this sounds (without thinking too much) like you are
doing reasonably well with LLR similarity.

Have you tried a factorizing recommender?


On Sun, Jul 14, 2013 at 10:49 PM, Jayesh <jayesh.sidhwani@gmail.com> wrote:

> 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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