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From Sean Owen <sro...@gmail.com>
Subject Re: Reproducibility, and Recommender Algorithms in Mahout
Date Sat, 30 Mar 2013 16:46:38 GMT
You should be able to get reproducible random seed values by calling
RandomUtils.useTestSeed() at the very start of your program. But if
your goal is to get an unbiased view of the quality of results, you
want to run several times and take the average yes.

On Sat, Mar 30, 2013 at 3:57 PM, Reinhard Denis Najogie
<najogie@gmail.com> wrote:
> Dear all,
>
> I am doing experiments as a part of my final project. I'm comparing the
> performance of Mahout's implementations of recommender algorithms on some
> public dataset (so far bookcross and grouplens). I want to ask 2 questions:
>
> 1. The score (RMSE) results quite vary each time I run an algorithm
> (sometimes +- 0.5 difference on some algorithms). Is there any way that I
> can make it produce the same result on each run? Maybe by setting a seed
> somewhere on the code? Or should I just do like 10 run and take the average
> score?
>
> 2. Where can I see the list of all recommender algorithms already
> implemented by Mahout? From what I read on Mahout in Action book, there are
> 6 algorithms: UserBased, ItemBased, Slope One, SVD, KnnItemBased, and
> TreeClustering. Are there new algorithms since then? Oh, and I found both
> KnnItem and TreeClustering are deprecated on the newest version of Mahout
> (0.8-SNAPSHOT) ? Why is this the case?
>
> --
> Regards,
> Reinhard Denis Najogie

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