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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] Created: (MAHOUT-321) Rationalize use of similarity metrics as weights in user-based, item-based recommendation
Date Wed, 03 Mar 2010 11:28:27 GMT
Rationalize use of similarity metrics as weights in user-based, item-based recommendation
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                 Key: MAHOUT-321
                 URL: https://issues.apache.org/jira/browse/MAHOUT-321
             Project: Mahout
          Issue Type: Improvement
          Components: Collaborative Filtering
    Affects Versions: 0.2
            Reporter: Sean Owen
            Assignee: Sean Owen
            Priority: Minor
             Fix For: 0.4


See this thread: http://old.nabble.com/weighted-score-td27686783.html

In short, using similarity values as weights in a weighted average is problematic since they
can be negative. This can result in weighted averages well out of range of possible preference
values, even infinite. The current solution simply moves the values from [-1,1] to [0,2] but
this has undesirable effects like giving weight of 1 to entities with no similarity.

Tamas advances, and Ted refined, a convincing argument that negative weights can be handled
in a different way which doesn't require them to be arbitrarily shifted. It is simply a matter
of capping the estimated preference at the max or min value the preference value can take
on.

It's possible for the framework to track this max/min value observed in the data, and do the
capping, with little performance impact. Hence I want to make this change after 0.3 is released.

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