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Lance Norskog edited comment on MAHOUT847 at 10/20/11 11:24 PM:

As a perpetual beginner, it is daunting to learn Mahout. It helps if welldefined math terms
are only used when the code matches the (200yearold) concept.
Perhaps _WarpedEuclideanSimilarity_ would be more clear?
was (Author: lancenorskog):
As a perpetual beginner, it is daunting to learn Mahout. It helps if welldefined math
terms are only used when the code matches the (200yearold) concept.
WarpedEuclideanSimilarity perhaps would be more clear.
> Improve Euclidean distance similarity calculation
> 
>
> Key: MAHOUT847
> URL: https://issues.apache.org/jira/browse/MAHOUT847
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.5
> Reporter: Sean Owen
> Assignee: Sean Owen
> Priority: Minor
> Labels: distance, euclidean, similarity, vector
> Fix For: 0.6
>
> Attachments: MAHOUT847.patch
>
>
> In the nondistributed recommender world, the Euclidean distance similarity is calculated
as n/(1+d), where d is distance and n is dimension. 1/(1+d) is a valid mapping from distance
[0,infinity) to similarity (0,1]. n is there to "correct" for the fact that things are farther
apart in higher dimensions. It would be righter, after some discussion, to use a factor of
sqrt(n), and apply directly to the distance; 1/(1+d/sqrt(n)).
> I propose fixing the calculation accordingly.
> In the distributed similarity, the formula is 11/(1+d), which is the wrong way around.
That will be fixed. I'd apply the same heuristic, except that at the moment we don't have
access to the value of n at that point. I don't like the inconsistency but it's minor; would
rather get this change in now, which definitely improves things.

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