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From Jayesh Sidhwani <jayeshsidhw...@shopsense.co>
Subject Re: Using Mahout for low-volume data
Date Mon, 15 Jul 2013 17:20:21 GMT
Okay. I'll try that and get back with the results.
Thank You

On Monday, July 15, 2013, Ted Dunning wrote:

> I think so, but I cannot say that I know so.
>
>
> On Mon, Jul 15, 2013 at 8:37 AM, Koobas <koobas@gmail.com <javascript:;>>
> wrote:
>
> > Is a factorizing recommender a better idea for low volume data in
> general?
> >
> >
> > On Mon, Jul 15, 2013 at 11:35 AM, Ted Dunning <ted.dunning@gmail.com<javascript:;>
> >
> > wrote:
> >
> > > 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<javascript:;>
> >
> > > 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<javascript:;>
> >
> > > > 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 <javascript:;>>
> > > > > 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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