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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