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From "Saikat Kanjilal (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-974) org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob use integer as userId and itemId
Date Thu, 06 Jun 2013 05:48:20 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-974?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13676722#comment-13676722
] 

Saikat Kanjilal commented on MAHOUT-974:
----------------------------------------

Sebastian,
Finally had a chance to dig into this further tonight, so in looking at the first two map-reduces
I see the ItemRatingVectorsMapper class, 2 ideas here: 1) should we get rid of this class
and just use the ItemIDIndexMapper class and try to make this class work for ALS 2) make ItemRatingVectorsMapper
handle the mapping, unlike ItemIDIndexMapper this class doesnt really handle an index and
deals with the rating matrix which itself would need to be modified.


Any thoughts on simplest solution?  My vote would be 2 but I need to read through the code
some more to get a deeper understanding.   Also please pardon if I'm way off base on solutioning
this :)), lot of code to read and understand
                
> org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob  use integer as userId
and itemId
> ---------------------------------------------------------------------------------------------------
>
>                 Key: MAHOUT-974
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-974
>             Project: Mahout
>          Issue Type: Wish
>          Components: Collaborative Filtering
>    Affects Versions: 0.8
>            Reporter: Han Hui Wen 
>            Assignee: Sebastian Schelter
>              Labels: CF,recommendation,als
>             Fix For: 0.8
>
>   Original Estimate: 2h
>  Remaining Estimate: 2h
>
> org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob  uses integer as userId
and itemId,but org.apache.mahout.cf.taste.hadoop.similarity.item.ItemSimilarityJob  and  org.apache.mahout.cf.taste.hadoop.item.RecommenderJob
.use Long as userId and ItemId.
> It's best that ParallelALSFactorizationJob   also uses Long as userId and itemId ,so
that same dataset can use all the recommendation arithrmetic

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