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https://issues.apache.org/jira/browse/MAHOUT-196?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12774025#action_12774025
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Sean Owen commented on MAHOUT-196:
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It's an interesting question, yeah. One approach would be to cap this in the
recommender, which makes some sense. Why would I ever estimate a movie was
rated 6 stars? the only catch is then you lose some ordering information that
the estimates provide. A 5.5 star movie should still be recommended before 5.4.
Let me think about a way to incorporate this. I imagine it is indeed just a
matter of exposing some way to express limits.
> 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
> Priority: Minor
>
> 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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