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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Consistent repeatable results for distributed ALS-WR recommender
Date Mon, 24 Jun 2013 19:46:43 GMT
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 <kazmikh@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: ssc@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" <kazmikh@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.
> > >
> > >
> > >
>

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