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https://issues.apache.org/jira/browse/MAHOUT-196?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved MAHOUT-196.
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Resolution: Fixed
Fix Version/s: 0.3
Assignee: Sean Owen
I committed a variant on your suggestion. Since these are optional parameters
to the object, I implemented them as bean properties rather than constructor
args (which I use for "necessary" parameters, though admittedly
inconsistently). You can now set a max/min preference value for use in the
evaluation.
> bounded values for RecommenderEvaluator
> ---------------------------------------
>
> Key: MAHOUT-196
> URL: https://issues.apache.org/jira/browse/MAHOUT-196
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Reporter: Jens Grivolla
> Assignee: Sean Owen
> Priority: Minor
> Fix For: 0.3
>
>
> When evaluating a recommender using RMSRecommenderEvaluator (or some others)
> on e.g. Netflix data, a recommender gets heavily penalized for predicting
> values below 1 or above 5 (that are known to be out of the permitted bounds).
> It seems to me that it makes no sense to change the recommender to avoid
> those predictions, since an estimated 6 probably has a greater chance to be
> highly rated than a predicted 5.1. I therefore propose to allow truncating
> predictions to those "legal" values directly in the evaluator and leave the
> recommenders unchanged, since it is more of a post-processing step than part
> of the recommender itself.
> I added those boundaries to the constructor of RMSRecommenderEvaluator and
> limit estimatedPreference to the allowed range before calculating
> "realPref.getValue() - estimatedPreference" and seem to get slightly better
> scores.
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