[jira] [Resolved] (MAHOUT-872) Revisit the parallel ALS matrix factorization

["Sebastian Schelter (Resolved) (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sebastian Schelter resolved MAHOUT-872.
---------------------------------------

       Resolution: Fixed
    Fix Version/s: 0.6
    
> 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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