org.apache.mahout.cf.taste.impl.similarity.CityBlockSimilarity Maven / Gradle / Ivy
/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.cf.taste.impl.similarity;
import java.util.Collection;
import org.apache.mahout.cf.taste.common.Refreshable;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.common.RefreshHelper;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.similarity.PreferenceInferrer;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
/**
* Implementation of City Block distance (also known as Manhattan distance) - the absolute value of the difference of
* each direction is summed. The resulting unbounded distance is then mapped between 0 and 1.
*/
public final class CityBlockSimilarity extends AbstractItemSimilarity implements UserSimilarity {
public CityBlockSimilarity(DataModel dataModel) {
super(dataModel);
}
/**
* @throws UnsupportedOperationException
*/
@Override
public void setPreferenceInferrer(PreferenceInferrer inferrer) {
throw new UnsupportedOperationException();
}
@Override
public void refresh(Collection alreadyRefreshed) {
Collection refreshed = RefreshHelper.buildRefreshed(alreadyRefreshed);
RefreshHelper.maybeRefresh(refreshed, getDataModel());
}
@Override
public double itemSimilarity(long itemID1, long itemID2) throws TasteException {
DataModel dataModel = getDataModel();
int preferring1 = dataModel.getNumUsersWithPreferenceFor(itemID1);
int preferring2 = dataModel.getNumUsersWithPreferenceFor(itemID2);
int intersection = dataModel.getNumUsersWithPreferenceFor(itemID1, itemID2);
return doSimilarity(preferring1, preferring2, intersection);
}
@Override
public double[] itemSimilarities(long itemID1, long[] itemID2s) throws TasteException {
DataModel dataModel = getDataModel();
int preferring1 = dataModel.getNumUsersWithPreferenceFor(itemID1);
double[] distance = new double[itemID2s.length];
for (int i = 0; i < itemID2s.length; ++i) {
int preferring2 = dataModel.getNumUsersWithPreferenceFor(itemID2s[i]);
int intersection = dataModel.getNumUsersWithPreferenceFor(itemID1, itemID2s[i]);
distance[i] = doSimilarity(preferring1, preferring2, intersection);
}
return distance;
}
@Override
public double userSimilarity(long userID1, long userID2) throws TasteException {
DataModel dataModel = getDataModel();
FastIDSet prefs1 = dataModel.getItemIDsFromUser(userID1);
FastIDSet prefs2 = dataModel.getItemIDsFromUser(userID2);
int prefs1Size = prefs1.size();
int prefs2Size = prefs2.size();
int intersectionSize = prefs1Size < prefs2Size ? prefs2.intersectionSize(prefs1) : prefs1.intersectionSize(prefs2);
return doSimilarity(prefs1Size, prefs2Size, intersectionSize);
}
/**
* Calculate City Block Distance from total non-zero values and intersections and map to a similarity value.
*
* @param pref1 number of non-zero values in left vector
* @param pref2 number of non-zero values in right vector
* @param intersection number of overlapping non-zero values
*/
private static double doSimilarity(int pref1, int pref2, int intersection) {
int distance = pref1 + pref2 - 2 * intersection;
return 1.0 / (1.0 + distance);
}
}