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