Mahout: UncenteredCosineSimilarity : Compute item similarity

It is an implementation of cosine similarity. Go through following article to know about cosine similarity.

https://en.wikipedia.org/wiki/Cosine_similarity

Let’s say I had following input data.

customer.csv

1,4,3
1,7,2
1,8,2
1,10,1
2,3,2
2,4,3
2,6,3
2,7,1
2,9,1
3,0,3
3,3,2
3,4,1
3,8,3
3,9,1
4,2,5
4,3,4
4,7,3
4,9,2
5,4,5
5,6,4
5,7,1
5,8,3

1,4,3 means customer 1 like item 4 and rated it 3

import java.io.File;
import java.io.IOException;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.similarity.UncenteredCosineSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;

public class UncenteredCosineSimilarityEx {
 public static String dataFile = "/Users/harikrishna_gurram/customer.csv";

 public static void main(String args[]) throws IOException, TasteException {

  DataModel model = new FileDataModel(new File(dataFile));

  UncenteredCosineSimilarity similarity = new UncenteredCosineSimilarity(model);

  long itemIds[] = { 3, 4, 6, 7, 8, 9, 10 };

  double distance[] = similarity.itemSimilarities(4, itemIds);

  for (int i = 0; i < itemIds.length; i++) {
   System.out.println("distance between item 4 and " + itemIds[i]
     + " is " + distance[i]);
  }

 }
}

Output

distance between item 4 and 3 is 0.8944271909999159
distance between item 4 and 4 is 1.0
distance between item 4 and 6 is 0.9946917938265513
distance between item 4 and 7 is 0.8716019289105665
distance between item 4 and 8 is 0.8648999641877366
distance between item 4 and 9 is 0.8944271909999159
distance between item 4 and 10 is 1.0

UncenteredCosineSimilarity returns NaN, if similarity is unknown.

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