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https://issues.apache.org/jira/browse/MAHOUT-925?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13168333#comment-13168333
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Anatoliy Kats commented on MAHOUT-925:
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We agree that there needs to be enough training data for a recommender to
output something, but you believe the cutoff should happen in the evaluator,
whereas I think the recommender should figure this out by itself, via some sort
of a threshold on the expected rating. For now, the distinction is mostly
theoretical to, so let's use what we already have. I'll change the reach
calculation as you suggest.
> Evaluate the reach of recommender algorithms
> --------------------------------------------
>
> Key: MAHOUT-925
> URL: https://issues.apache.org/jira/browse/MAHOUT-925
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.5
> Reporter: Anatoliy Kats
> Assignee: Sean Owen
> Priority: Minor
> Attachments: MAHOUT-925.patch
>
> Original Estimate: 1h
> Remaining Estimate: 1h
>
> The evaluation of a CF algorithm should include reach, the proportion of
> users for whom a recommendation could be made. An algorithm usually has a
> cutoff value on the confidence of the recommender, and if it is not high
> enough, no recommendation is made. The number of requested recommendations,
> or this parameter could be varied as part of the evaluation. The proposed
> patch adds this.
> My build with this patch breaks
> testMapper(org.apache.mahout.classifier.df.mapreduce.partial.Step1MapperTest):
> org.apache.mahout.classifier.df.node.Leaf.<init>(I)V . The test seems
> unrelated to the patch, so I am assuming this is broken in the trunk head as
> well. Unfortunately I am under a deadline, and I do not have time to write
> tests for the patch.
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