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From Michael Kazekin <kazm...@hotmail.com>
Subject RE: Consistent repeatable results for distributed ALS-WR recommender
Date Mon, 24 Jun 2013 19:44:15 GMT
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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