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From "Hudson (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-872) Revisit the parallel ALS matrix factorization
Date Fri, 04 Nov 2011 11:43:00 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-872?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13143934#comment-13143934
] 

Hudson commented on MAHOUT-872:
-------------------------------

Integrated in Mahout-Quality #1147 (See [https://builds.apache.org/job/Mahout-Quality/1147/])
    MAHOUT-872 Revisit the parallel ALS matrix factorization

ssc : http://svn.apache.org/viewcvs.cgi/?root=Apache-SVN&view=rev&rev=1197433
Files : 
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/ALSUtils.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/DatasetSplitter.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/FactorizationEvaluator.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/FeatureVectorWithRatingWritable.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/IndexedVarIntWritable.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/ParallelALSFactorizationJob.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/PredictionJob.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/RecommenderJob.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/TaggedVarIntWritable.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/VectorWithIndexWritable.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/eval
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/item/AggregateAndRecommendReducer.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/common/AbstractJob.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/common/mapreduce/MergeVectorsCombiner.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/common/mapreduce/MergeVectorsReducer.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/common/mapreduce/TransposeMapper.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/common/mapreduce/VectorSumReducer.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/math/VectorWritable.java
* /mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/hadoop/als/FeatureVectorWithRatingWritableTest.java
* /mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/hadoop/als/ParallelALSFactorizationJobTest.java
* /mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/hadoop/als/PredictionJobTest.java
* /mahout/trunk/examples/bin/factorize-movielens-1M.sh
* /mahout/trunk/integration/src/test/java/org/apache/mahout/utils/eval
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/als/AlternateLeastSquaresSolver.java
* /mahout/trunk/src/conf/driver.classes.props
* /mahout/trunk/src/conf/recommendfactorized.props

                
> 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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