For relatively small amounts of data, the effect of randomness may be large. For larger amounts of data, the effects should be small.
On Sat, Mar 23, 2013 at 1:35 PM, Sebastian Schelter <[email protected] > wrote: > Hi Nadia, > > This should be the case if you fix the seed for the random number > generator. What do you mean by the "results differed"? How did you > choose the single item that you looked at? How did you choose the number > of iterations? > > Best, > Sebastian > > > > > On 23.03.2013 13:10, Nadia A Najjar wrote: > > Hi, > > to rephrase my question: > > Does the SVD++ factorization produce the same prediction value for a > given item every time the SVD recommender is run? > > > > I'd appreciate any insight on this! > > > > Thanks! > > > > > >> From: [email protected] > >> To: [email protected] > >> Subject: RE: Persistent SVD predictions > >> Date: Wed, 20 Mar 2013 01:32:35 -0400 > >> > >> Hi,This is when comparing a single prediction for an item. I seeded the > initial model training. That was the only place I found where a random > number was used. > >> > >>> Date: Tue, 19 Mar 2013 19:34:16 +0100 > >>> From: [email protected] > >>> To: [email protected] > >>> Subject: Re: Persistent SVD predictions > >>> > >>> Hi Nadia, > >>> > >>> how did you try to seed the factorization? How do you measure your > result? > >>> > >>> > >>> On 19.03.2013 19:04, Nadia A Najjar wrote: > >>>> Is there a way to get the same preference prediction result for an > item using SVD recommender with SVD++ factorization?I tried seeding the > factorization but the results still differed! > >>>> Thanks! > >>>> > >>> > >> > > > > > >
