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Posted to dev@mahout.apache.org by "Sebastian Schelter (JIRA)" <ji...@apache.org> on 2011/03/23 23:51:06 UTC

[jira] [Assigned] (MAHOUT-572) Non-distributed implementation of ALS-WR matrix factorization

     [ https://issues.apache.org/jira/browse/MAHOUT-572?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sebastian Schelter reassigned MAHOUT-572:
-----------------------------------------

    Assignee: Sebastian Schelter

> Non-distributed implementation of ALS-WR matrix factorization
> -------------------------------------------------------------
>
>                 Key: MAHOUT-572
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-572
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Collaborative Filtering
>    Affects Versions: 0.5
>            Reporter: Sebastian Schelter
>            Assignee: Sebastian Schelter
>             Fix For: 0.5
>
>         Attachments: MAHOUT-572-2.patch, MAHOUT-572.patch
>
>
> I created a non-distributed implementation of the algorithm described in "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.
> This gives more choice for users that need an online SVD Recommender and might be a useful starting point to implementing code that automatically finds a near-optimal regularization parameter for the distributed version of the algorithm in MAHOUT-542.
> Along with the patch goes a refactoring and polishing of the SVD Recommender code that separates the factorization computation from the Recommender implementation. This way it should easily be possible to try out different factorization approaches with our SVDRecommender.

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