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From "Anatoliy Kats (Commented) (JIRA)" <>
Subject [jira] [Commented] (MAHOUT-925) Evaluate the reach of recommender algorithms
Date Tue, 13 Dec 2011 12:41:30 GMT


Anatoliy Kats commented on MAHOUT-925:

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:
>             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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