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 <[email protected]>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: [email protected] > > To: [email protected] > > > > 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" <[email protected]>: > > > > > 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. > > > > > > > > > >
