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From "Danny Bickson (JIRA)" <>
Subject [jira] Commented: (MAHOUT-542) MapReduce implementation of ALS-WR
Date Tue, 15 Feb 2011 16:38:57 GMT


Danny Bickson commented on MAHOUT-542:

Problem solved - 
1)I have increased heap size to 4GB
2)Moved to a larger instance: m2.2xlarge
3)Increased children mappers memory to 2GB

One or more of those changes fixed the memory error.

One iteration using the full netflix data takes around 75 minutes.
RMSE looks good: 1.04 after one iteration.

- Danny

> MapReduce implementation of ALS-WR
> ----------------------------------
>                 Key: MAHOUT-542
>                 URL:
>             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,
> 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
> 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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