On Mon, Jun 24, 2013 at 1:07 PM, Michael Kazekin <[email protected]>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: [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. > > > > > > > > > > > > > > > > > > > >
