/**
* 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.recommender;
import java.util.Collection;
import java.util.List;
import java.util.concurrent.Callable;
import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;
import org.apache.mahout.cf.taste.common.NoSuchUserException;
import org.apache.mahout.cf.taste.common.Refreshable;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.common.FullRunningAverage;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.common.RefreshHelper;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.google.common.base.Preconditions;
/**
* <p>
* A simple recommender that always estimates preference for an item to be the average of all known preference
* values for that item. No information about users is taken into account. This implementation is provided for
* experimentation; while simple and fast, it may not produce very good recommendations.
* </p>
*/
public final class ItemAverageRecommender extends AbstractRecommender {
private static final Logger log = LoggerFactory.getLogger(ItemAverageRecommender.class);
private final FastByIDMap<RunningAverage> itemAverages;
private final ReadWriteLock buildAveragesLock;
private final RefreshHelper refreshHelper;
public ItemAverageRecommender(DataModel dataModel) throws TasteException {
super(dataModel);
this.itemAverages = new FastByIDMap<RunningAverage>();
this.buildAveragesLock = new ReentrantReadWriteLock();
this.refreshHelper = new RefreshHelper(new Callable<Object>() {
@Override
public Object call() throws TasteException {
buildAverageDiffs();
return null;
}
});
refreshHelper.addDependency(dataModel);
buildAverageDiffs();
}
@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
log.debug("Recommending items for user ID '{}'", userID);
PreferenceArray preferencesFromUser = getDataModel().getPreferencesFromUser(userID);
FastIDSet possibleItemIDs = getAllOtherItems(userID, preferencesFromUser);
TopItems.Estimator<Long> estimator = new Estimator();
List<RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
estimator);
log.debug("Recommendations are: {}", topItems);
return topItems;
}
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
DataModel dataModel = getDataModel();
Float actualPref = dataModel.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
return doEstimatePreference(itemID);
}
private float doEstimatePreference(long itemID) {
buildAveragesLock.readLock().lock();
try {
RunningAverage average = itemAverages.get(itemID);
return average == null ? Float.NaN : (float) average.getAverage();
} finally {
buildAveragesLock.readLock().unlock();
}
}
private void buildAverageDiffs() throws TasteException {
try {
buildAveragesLock.writeLock().lock();
DataModel dataModel = getDataModel();
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
PreferenceArray prefs = dataModel.getPreferencesFromUser(it.nextLong());
int size = prefs.length();
for (int i = 0; i < size; i++) {
long itemID = prefs.getItemID(i);
RunningAverage average = itemAverages.get(itemID);
if (average == null) {
average = new FullRunningAverage();
itemAverages.put(itemID, average);
}
average.addDatum(prefs.getValue(i));
}
}
} finally {
buildAveragesLock.writeLock().unlock();
}
}
@Override
public void setPreference(long userID, long itemID, float value) throws TasteException {
DataModel dataModel = getDataModel();
double prefDelta;
try {
Float oldPref = dataModel.getPreferenceValue(userID, itemID);
prefDelta = oldPref == null ? value : value - oldPref;
} catch (NoSuchUserException nsee) {
prefDelta = value;
}
super.setPreference(userID, itemID, value);
try {
buildAveragesLock.writeLock().lock();
RunningAverage average = itemAverages.get(itemID);
if (average == null) {
RunningAverage newAverage = new FullRunningAverage();
newAverage.addDatum(prefDelta);
itemAverages.put(itemID, newAverage);
} else {
average.changeDatum(prefDelta);
}
} finally {
buildAveragesLock.writeLock().unlock();
}
}
@Override
public void removePreference(long userID, long itemID) throws TasteException {
DataModel dataModel = getDataModel();
Float oldPref = dataModel.getPreferenceValue(userID, itemID);
super.removePreference(userID, itemID);
if (oldPref != null) {
try {
buildAveragesLock.writeLock().lock();
RunningAverage average = itemAverages.get(itemID);
if (average == null) {
throw new IllegalStateException("No preferences exist for item ID: " + itemID);
} else {
average.removeDatum(oldPref);
}
} finally {
buildAveragesLock.writeLock().unlock();
}
}
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "ItemAverageRecommender";
}
private final class Estimator implements TopItems.Estimator<Long> {
@Override
public double estimate(Long itemID) {
return doEstimatePreference(itemID);
}
}
}