Thank you, Ted!
Any feedback on the usefulness of such functionality? Could it increase the
'playability' of the recommender?
> From: ted.dunn...@gmail.com
> Date: Mon, 24 Jun 2013 20:46:43 +0100
> Subject: Re: Consistent repeatable results for distributed ALS-WR recommender
> To: user@mahout.apache.org
>
> See org.apache.mahout.common.RandomUtils#useTestSeed
>
> It provides the ability to freeze the initial seed. Normally this is only
> used during testing, but you could use it.
>
>
> On Mon, Jun 24, 2013 at 8:44 PM, Michael Kazekin <kazm...@hotmail.com>wrote:
>
> > Thanks a lot!
> > Do you know by any chance what are the underlying reasons for including
> > such mandatory random seed initialization?
> > Do you see any sense in providing another option, such as filling them
> > with zeroes in order to ensure the consistency and repeatability? (for
> > example we might want to track and compare the generated recommendation
> > lists for different parameters, such as the number of features or number of
> > iterations etc.)
> > M.
> >
> >
> > > Date: Mon, 24 Jun 2013 19:51:44 +0200
> > > Subject: Re: Consistent repeatable results for distributed ALS-WR
> > recommender
> > > From: s...@apache.org
> > > To: user@mahout.apache.org
> > >
> > > The matrices of the factorization are initalized randomly. If you fix the
> > > random seed (would require modification of the code) you should get
> > exactly
> > > the same results.
> > > Am 24.06.2013 13:49 schrieb "Michael Kazekin" <kazm...@hotmail.com>:
> > >
> > > > Hi!
> > > > Should I assume that under same dataset and same parameters for
> > factorizer
> > > > and recommender I will get the same results for any specific user?
> > > > My current understanding that theoretically ALS-WR algorithm could
> > > > guarantee this, but I was wondering could be there any numeric method
> > > > issues and/or implementation-specific concerns.
> > > > Would appreciate any highlight on this issue.
> > > > Mike.
> > > >
> > > >
> > > >
> >
