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https://issues.apache.org/jira/browse/MAHOUT-906?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13169361#comment-13169361
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Sean Owen commented on MAHOUT-906:
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Sure, you can do that. I am not sure that gives you a better result than
anything other split, but at least it is no worse. I'm reluctant to add a hook
that just lets someone create an invalid test (with ratings) but if that can be
worked around (throw an exception if the model has no pref values?) then that's
mitigated.
This would really be much more useful in the estimate-based evaluation though,
not here. I thought that's what this was about.
I still have no understanding of why this refactoring involves adding new eval
classes or moving or renaming them. It just factoring out a piece of logic that
splits a set into two subsets. It ought not be more complex than that.
> Allow collaborative filtering evaluators to use custom logic in splitting
> data set
> ----------------------------------------------------------------------------------
>
> Key: MAHOUT-906
> URL: https://issues.apache.org/jira/browse/MAHOUT-906
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.5
> Reporter: Anatoliy Kats
> Priority: Minor
> Labels: features
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> I want to start a discussion about factoring out the logic used in splitting
> the data set into training and testing. Here is how things stand: There are
> two independent evaluator based classes:
> AbstractDifferenceRecommenderEvaluator, splits all the preferences randomly
> into a training and testing set. GenericRecommenderIRStatsEvaluator takes
> one user at a time, removes their top AT preferences, and counts how many of
> them the system recommends back.
> I have two use cases that both deal with temporal dynamics. In one case,
> there may be expired items that can be used for building a training model,
> but not a test model. In the other, I may want to simulate the behavior of a
> real system by building a preference matrix on days 1-k, and testing on the
> ratings the user generated on the day k+1. In this case, it's not items, but
> preferences(user, item, rating triplets) which may belong only to the
> training set. Before we discuss appropriate design, are there any other use
> cases we need to keep in mind?
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