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From "Sean Owen (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-906) Allow collaborative filtering evaluators to use custom logic in splitting data set
Date Thu, 15 Dec 2011 10:54:30 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-906?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13170119#comment-13170119
] 

Sean Owen commented on MAHOUT-906:
----------------------------------

OK, sounds like you want to replace more logic, but that's not much harder. Even in the IR
test, it's still a question of separating data into test and training data. Hopefully you
can then re-use the same test-training split logic for both objects. You do not need to modify
any Recommender here.
                
> 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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