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From "Sean Owen (Commented) (JIRA)" <>
Subject [jira] [Commented] (MAHOUT-906) Allow collaborative filtering evaluators to use custom logic in splitting data set
Date Wed, 14 Dec 2011 12:25:30 GMT


Sean Owen commented on MAHOUT-906:

OK. I think we're speaking about the estimation test, not the IR tests. In the IR test there
is not really a notion of training and test data; there are the relevant items and non-relevant
items. The 'relevant' items are the ones held out. You could hold out the latest prefs, I
guess, though I wonder if this compromises the meaning of the result. It is not necessarily
"bad", for example, if the recommender doesn't consider those latest prefs the top recs. That
is not what any implementation is trying to do.

Sorting isn't needed, but it is probably the easiest way to split the data into training and
test data. I don't know if it will be much slower than alternatives, and if it's not, fine
for eval purposes. TopN is an existing class. It will be faster at picking out the "most recent"
prefs for you but I don't know of an easy way to reuse it to also give you the rest of the
older objects efficiently. So, I suppose I'd start with a sort, which is probably 10 lines
of code, and see if it's fast enough.

I do not see a need for any new evaluator, no. The point here is to factor out the test/training
split logic only, and with that pluggable, you should be able to create test/training splits
based on time. No?
> Allow collaborative filtering evaluators to use custom logic in splitting data set
> ----------------------------------------------------------------------------------
>                 Key: MAHOUT-906
>                 URL:
>             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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