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From "Sebastian Schelter (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-872) Revisit the parallel ALS matrix factorization
Date Fri, 04 Nov 2011 09:39:00 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-872?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13143869#comment-13143869
] 

Sebastian Schelter commented on MAHOUT-872:
-------------------------------------------

High level documention added at https://cwiki.apache.org/confluence/display/MAHOUT/Collaborative+Filtering+with+AlS-WR
                
> Revisit the parallel ALS matrix factorization
> ---------------------------------------------
>
>                 Key: MAHOUT-872
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-872
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Collaborative Filtering
>    Affects Versions: 0.6
>            Reporter: Sebastian Schelter
>            Assignee: Sebastian Schelter
>             Fix For: 0.6
>
>
> Our current code for computing a decomposition of a rating matrix with Alternating Least
Squares (ALS) uses a lot of highly unefficient reduce side joins. 
> The rating matrix A is decomposed into a matrix U of users x features and a matrix M
of items x features. Each of these matrices is iteratively recomputed until a maximum number
of iterations is reached
> If we assume that U and M fit into the memory of a single mapper instance, each iteration
can be implemented as single map-only job, which greatly improves the runtime of this job.
> Note that in spite of these improvements this job is still rather slow as Hadoop is a
poor fit for iterative algorithms. Each iteration has to be scheduled again and data is always
read from and written to disk.

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