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https://issues.apache.org/jira/browse/MAHOUT-542?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sebastian Schelter updated MAHOUT-542:
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Attachment: MAHOUT-542-5.patch
updated the patch to work with the current trunk. I only had to remove
AlternateLeastSquaresSolver as this class was already committed.
btw: the call to fail() in the unit test is valid, it's used to make a test
fail when a exception is not thrown that you expect to be thrown
It would be great if you use the current version of this patch to run the
factorization on the netflix dataset with the parameters described in the
paper. I'd love to see if we get roughly the same results.
> MapReduce implementation of ALS-WR
> ----------------------------------
>
> Key: MAHOUT-542
> URL: https://issues.apache.org/jira/browse/MAHOUT-542
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Affects Versions: 0.5
> Reporter: Sebastian Schelter
> Attachments: MAHOUT-452.patch, MAHOUT-542-2.patch,
> MAHOUT-542-3.patch, MAHOUT-542-4.patch, MAHOUT-542-5.patch
>
>
> As Mahout is currently lacking a distributed collaborative filtering
> algorithm that uses matrix factorization, I spent some time reading through a
> couple of the Netflix papers and stumbled upon the "Large-scale Parallel
> Collaborative Filtering for the Netflix Prize" available at
> http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf.
> It describes a parallel algorithm that uses "Alternating-Least-Squares with
> Weighted-λ-Regularization" to factorize the preference-matrix and gives some
> insights on how the authors distributed the computation using Matlab.
> It seemed to me that this approach could also easily be parallelized using
> Map/Reduce, so I sat down and created a prototype version. I'm not really
> sure I got the mathematical details correct (they need some optimization
> anyway), but I wanna put up my prototype implementation here per Yonik's law
> of patches.
> Maybe someone has the time and motivation to work a little on this with me.
> It would be great if someone could validate the approach taken (I'm willing
> to help as the code might not be intuitive to read) and could try to
> factorize some test data and give feedback then.
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