Yann Moisan created MAHOUT-1090:
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Summary: Add a similarity implementation that computes cosine over
all entries
Key: MAHOUT-1090
URL: https://issues.apache.org/jira/browse/MAHOUT-1090
Project: Mahout
Issue Type: New Feature
Components: Collaborative Filtering
Affects Versions: 0.7
Reporter: Yann Moisan
Assignee: Sean Owen
Priority: Minor
The aim of this feature is to use a recommender to compute similarities as the
hadoop RowSimilarityJob. It will be faster for small dataset because in-memory.
So we need an in-memory implementation of the Cosine Similarity which computes
cosine over all entries (UncenteredCosineSimilarity use only entries that are
in both vectors).
Here is my implementation (doesn't support refresh for the moment):
import java.util.Collection;
import java.util.HashMap;
import java.util.Map;
import org.apache.mahout.cf.taste.common.Refreshable;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.similarity.AbstractItemSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
public class CosineSimilarity extends AbstractItemSimilarity {
protected CosineSimilarity(DataModel dataModel) {
super(dataModel);
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
throw new UnsupportedOperationException();
}
@Override
public double itemSimilarity(long itemID1, long itemID2) throws
TasteException {
DataModel model = getDataModel();
PreferenceArray xPrefs = model.getPreferencesForItem(itemID1);
PreferenceArray yPrefs = model.getPreferencesForItem(itemID2);
double sumXY = 0;
double sumX2 = 0;
double sumY2 = 0;
Map<Long, Float> mX = new HashMap<Long, Float>();
for (int xPrefIndex = 0; xPrefIndex < xPrefs.length(); xPrefIndex++) {
float x = xPrefs.get(xPrefIndex).getValue();
mX.put(xPrefs.get(xPrefIndex).getUserID(), x);
sumX2 += x * x;
}
for (int yPrefIndex = 0; yPrefIndex < yPrefs.length(); yPrefIndex++) {
float y = yPrefs.get(yPrefIndex).getValue();
Float x = mX.get(yPrefs.get(yPrefIndex).getUserID());
if (x != null) {
sumXY += x * y;
}
sumY2 += y * y;
}
return sumXY / (Math.sqrt(sumX2) * Math.sqrt(sumY2));
}
@Override
public double[] itemSimilarities(long itemID1, long[] itemID2s) throws
TasteException {
int length = itemID2s.length;
double[] result = new double[length];
for (int i = 0; i < length; i++) {
result[i] = itemSimilarity(itemID1, itemID2s[i]);
}
return result;
}
}
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