Thank you, Ted! Any feedback on the usefulness of such functionality? Could it increase the 'playability' of the recommender?
> From: [email protected] > Date: Mon, 24 Jun 2013 20:46:43 +0100 > Subject: Re: Consistent repeatable results for distributed ALS-WR recommender > To: [email protected] > > 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. > > > > > > > > > > > > > >
