/**
* 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 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.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.PreferenceInferrer;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import java.util.Collection;
/** See <a href="http://citeseer.ist.psu.edu/29096.html">http://citeseer.ist.psu.edu/29096.html</a>. */
public final class LogLikelihoodSimilarity implements UserSimilarity, ItemSimilarity {
private final DataModel dataModel;
public LogLikelihoodSimilarity(DataModel dataModel) {
this.dataModel = dataModel;
}
/**
* @throws UnsupportedOperationException
*/
@Override
public void setPreferenceInferrer(PreferenceInferrer inferrer) {
throw new UnsupportedOperationException();
}
@Override
public double userSimilarity(long userID1, long userID2) throws TasteException {
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);
if (intersectionSize == 0) {
return Double.NaN;
}
int numItems = dataModel.getNumItems();
double logLikelihood = twoLogLambda(intersectionSize,
prefs1Size - intersectionSize,
prefs2Size,
numItems - prefs2Size);
return 1.0 - 1.0 / (1.0 + logLikelihood);
}
@Override
public double itemSimilarity(long itemID1, long itemID2) throws TasteException {
int preferring1and2 = dataModel.getNumUsersWithPreferenceFor(itemID1, itemID2);
if (preferring1and2 == 0) {
return Double.NaN;
}
int preferring1 = dataModel.getNumUsersWithPreferenceFor(itemID1);
int preferring2 = dataModel.getNumUsersWithPreferenceFor(itemID2);
int numUsers = dataModel.getNumUsers();
double logLikelihood =
twoLogLambda(preferring1and2, preferring1 - preferring1and2, preferring2, numUsers - preferring2);
return 1.0 - 1.0 / (1.0 + logLikelihood);
}
static double twoLogLambda(double k1, double k2, double n1, double n2) {
double p = (k1 + k2) / (n1 + n2);
return 2.0 * (logL(k1 / n1, k1, n1) + logL(k2 / n2, k2, n2) - logL(p, k1, n1) - logL(p, k2, n2));
}
private static double logL(double p, double k, double n) {
return k * safeLog(p) + (n - k) * safeLog(1.0 - p);
}
private static double safeLog(double d) {
return d <= 0.0 ? 0.0 : Math.log(d);
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
alreadyRefreshed = RefreshHelper.buildRefreshed(alreadyRefreshed);
RefreshHelper.maybeRefresh(alreadyRefreshed, dataModel);
}
@Override
public String toString() {
return "LogLikelihoodSimilarity[dataModel:" + dataModel + ']';
}
}