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https://issues.apache.org/jira/browse/MAHOUT-305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12860914#action_12860914
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Sean Owen commented on MAHOUT-305:
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OK, I think I get the (item1,item2) -> (item2,count) part, at least, why it can
be used in conjunction with a one-pass solution.
I wasn't sure how you guarantee that all (item1,item2) for item1 arrive at the
same reducer. But the answer is the partitioner?
Then it works; I still think there is a need for lots of pruning and big
combiner buffers after the map but that's different.
Am I right that (item1,item2) -> count is all that's needed?
> Combine both cooccurrence-based CF M/R jobs
> -------------------------------------------
>
> Key: MAHOUT-305
> URL: https://issues.apache.org/jira/browse/MAHOUT-305
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.2
> Reporter: Sean Owen
> Assignee: Ankur
> Priority: Minor
>
> We have two different but essentially identical MapReduce jobs to make
> recommendations based on item co-occurrence:
> org.apache.mahout.cf.taste.hadoop.{item,cooccurrence}. They ought to be
> merged. Not sure exactly how to approach that but noting this in JIRA, per
> Ankur.
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