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https://issues.apache.org/jira/browse/MAHOUT-466?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12897773#action_12897773
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Sebastian Schelter commented on MAHOUT-466:
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Could I ask you to provide a patch and some test data that shows the
performance increases?
> simplify or alternative Similarity
> arithmetic(AbstractDistributedVectorSimilarity) for boolean data
> ----------------------------------------------------------------------------------------------------
>
> Key: MAHOUT-466
> URL: https://issues.apache.org/jira/browse/MAHOUT-466
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.4
> Reporter: Hui Wen Han
> Fix For: 0.4
>
>
> For boolean data ,the prefValue is always 1.0f, We need simplify Similarity
> arithmetic
> for example:
> 1) DistributedEuclideanDistanceVectorSimilarity
> package org.apache.mahout.math.hadoop.similarity.vector;
> import org.apache.mahout.math.hadoop.similarity.Cooccurrence;
> /**
> * distributed implementation of euclidean distance as vector similarity
> measure
> */
> public class DistributedEuclideanDistanceVectorSimilarity extends
> AbstractDistributedVectorSimilarity {
> @Override
> protected double doComputeResult(int rowA, int rowB, Iterable<Cooccurrence>
> cooccurrences, double weightOfVectorA,
> double weightOfVectorB, int numberOfColumns) {
> double n = 0.0;
> double sumXYdiff2 = 0.0;
> for (Cooccurrence cooccurrence : cooccurrences) { double diff =
> cooccurrence.getValueA() - cooccurrence.getValueB(); sumXYdiff2 += diff *
> diff; n++; }
> return n / (1.0 + Math.sqrt(sumXYdiff2));
> }
> }
> this one is always return n (=cooccurrence.size())
> 2) DistributedUncenteredCosineVectorSimilarity
> /**
> * distributed implementation of cosine similarity that does not center
> its data
> */
> public class DistributedUncenteredCosineVectorSimilarity extends
> AbstractDistributedVectorSimilarity {
> @Override
> protected double doComputeResult(int rowA, int rowB, Iterable<Cooccurrence>
> cooccurrences, double weightOfVectorA,
> double weightOfVectorB, int numberOfColumns) {
> int n = 0;
> double sumXY = 0.0;
> double sumX2 = 0.0;
> double sumY2 = 0.0;
> for (Cooccurrence cooccurrence : cooccurrences) { double x =
> cooccurrence.getValueA(); double y = cooccurrence.getValueB(); sumXY += x *
> y; sumX2 += x * x; sumY2 += y * y; n++; }
> if (n == 0) { return Double.NaN; }
> double denominator = Math.sqrt(sumX2) * Math.sqrt(sumY2);
> if (denominator == 0.0) { // One or both vectors has -all- the same values;
> // can't really say much similarity under this measure return Double.NaN; }
> return sumXY / denominator;
> }
> }
> this one will always return 1.0
> 3) DistributedUncenteredZeroAssumingCosineVectorSimilarity
> If n users like ItemA, m users like ItemB,p users like both ItemA and ItemB,
> DistributedUncenteredZeroAssumingCosineVectorSimilarity return p/(m*n).
> it also can use for Boolean data, but we can provide a simple one , return
> (p*p)/(m*n),no so much computing.
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