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https://issues.apache.org/jira/browse/MAHOUT-305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12836689#action_12836689
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Sean Owen commented on MAHOUT-305:
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Yes there are some prolific users. I don't have anything ready-made for such a
test; the existing eval framework won't work here. I think it would need a bit
of coding to pull out some test data, run the job, compare the results.
I have only one little tweak to make to the procedure you mention here. Really,
we ought to pull out the most-preferred movies as test data. After all the
recommendations will be for those movies that should be rated highly. We
wouldn't want to punish the algorithm for failing to recommend something I have
rated, but didn't like, over something I haven't rated but indeed would like.
One very crude way to do this is remove all 5-star ratings in the data set, and
see how many of those actually come back in the recommendations.
> 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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