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https://issues.apache.org/jira/browse/MAHOUT-847?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13133234#comment-13133234
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Lance Norskog commented on MAHOUT-847:
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How does this compare with EuclideanDistanceMeasure? Does EDM have the same
semantics as the old or new EDSimilarity? If I use one or the other in
parallel-structured recommender stacks should I expect roughly the same
results? Or, should EDM be rewritten to match?
> Improve Euclidean distance similarity calculation
> -------------------------------------------------
>
> Key: MAHOUT-847
> URL: https://issues.apache.org/jira/browse/MAHOUT-847
> 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: MAHOUT-847.patch
>
>
> In the non-distributed 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 right-er, 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 1-1/(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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