I agree with you, I should have mentioned earlier that it would be good to
separate "noise from data" and deal with only what is separable. Of course
there is no truly deterministic implementation of any algorithm, but I would
expect to see "credible" results on a macro-level (in our case it would be nice
to see the same order of recommendations given the fixed seed). It seems
important for experiments (and for testing, as mentioned), isn't it?
Another question is that afaik ALS-WR is deterministic by its inception, so I'm
trying to understand the reasons (and I'm assuming there are some) for the
specific implementation design.
Thanks for a free lunch! ;)
Cheers,Mike.
> Date: Mon, 24 Jun 2013 13:13:20 -0700
> Subject: Re: Consistent repeatable results for distributed ALS-WR recommender
> From: dlie...@gmail.com
> To: user@mahout.apache.org
>
> On Mon, Jun 24, 2013 at 1:07 PM, Michael Kazekin <kazm...@hotmail.com>wrote:
>
> > Thank you, Ted!
> > Any feedback on the usefulness of such functionality? Could it increase
> > the 'playability' of the recommender?
> >
>
> Almost all methods -- even deterministic ones -- will have a "credible
> interval" of prediction simply because method assumptions do not hold 100%
> in real life, real data. So what you really want to know in such cases is
> the credible interval rather than whether method is deterministic or not.
> Non-deterministic methods might very well be more accurate than
> deterministic ones in this context, and, therefore, more "useful". Also
> see: "no free lunch theorem".
>
>
> > > 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.
> > > > > >
> > > > > >
> > > > > >
> > > >
> >
> >
