/**
* 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 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.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Rescorer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
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;
/**
* <p>Like {@link ItemAverageRecommender}, except that estimated preferences are adjusted for the users' average
* preference value. For example, say user X has not rated item Y. Item Y's average preference value is 3.5. User X's
* average preference value is 4.2, and the average over all preference values is 4.0. User X prefers items 0.2 higher
* on average, so, the estimated preference for user X, item Y is 3.5 + 0.2 = 3.7.</p>
*/
public final class ItemUserAverageRecommender extends AbstractRecommender {
private static final Logger log = LoggerFactory.getLogger(ItemUserAverageRecommender.class);
private final FastByIDMap<RunningAverage> itemAverages;
private final FastByIDMap<RunningAverage> userAverages;
private final RunningAverage overallAveragePrefValue;
private boolean averagesBuilt;
private final ReadWriteLock buildAveragesLock;
private final RefreshHelper refreshHelper;
public ItemUserAverageRecommender(DataModel dataModel) {
super(dataModel);
this.itemAverages = new FastByIDMap<RunningAverage>();
this.userAverages = new FastByIDMap<RunningAverage>();
this.overallAveragePrefValue = new FullRunningAverage();
this.buildAveragesLock = new ReentrantReadWriteLock();
this.refreshHelper = new RefreshHelper(new Callable<Object>() {
@Override
public Object call() throws TasteException {
buildAverageDiffs();
return null;
}
});
refreshHelper.addDependency(dataModel);
}
@Override
public List<RecommendedItem> recommend(long userID, int howMany, Rescorer<Long> rescorer)
throws TasteException {
if (howMany < 1) {
throw new IllegalArgumentException("howMany must be at least 1");
}
log.debug("Recommending items for user ID '{}'", userID);
checkAverageDiffsBuilt();
FastIDSet possibleItemIDs = getAllOtherItems(userID);
TopItems.Estimator<Long> estimator = new Estimator(userID);
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;
}
checkAverageDiffsBuilt();
return doEstimatePreference(userID, itemID);
}
private float doEstimatePreference(long userID, long itemID) {
buildAveragesLock.readLock().lock();
try {
RunningAverage itemAverage = itemAverages.get(itemID);
if (itemAverage == null) {
return Float.NaN;
}
RunningAverage userAverage = userAverages.get(userID);
if (userAverage == null) {
return Float.NaN;
}
double userDiff = userAverage.getAverage() - overallAveragePrefValue.getAverage();
return (float) (itemAverage.getAverage() + userDiff);
} finally {
buildAveragesLock.readLock().unlock();
}
}
private void checkAverageDiffsBuilt() throws TasteException {
if (!averagesBuilt) {
buildAverageDiffs();
}
}
private void buildAverageDiffs() throws TasteException {
try {
buildAveragesLock.writeLock().lock();
DataModel dataModel = getDataModel();
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
long userID = it.nextLong();
PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
int size = prefs.length();
for (int i = 0; i < size; i++) {
long itemID = prefs.getItemID(i);
float value = prefs.getValue(i);
addDatumAndCreateIfNeeded(itemID, value, itemAverages);
addDatumAndCreateIfNeeded(userID, value, userAverages);
overallAveragePrefValue.addDatum(value);
}
}
averagesBuilt = true;
} finally {
buildAveragesLock.writeLock().unlock();
}
}
private static void addDatumAndCreateIfNeeded(long itemID,
float value,
FastByIDMap<RunningAverage> averages) {
RunningAverage itemAverage = averages.get(itemID);
if (itemAverage == null) {
itemAverage = new FullRunningAverage();
averages.put(itemID, itemAverage);
}
itemAverage.addDatum(value);
}
@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 itemAverage = itemAverages.get(itemID);
if (itemAverage == null) {
RunningAverage newItemAverage = new FullRunningAverage();
newItemAverage.addDatum(prefDelta);
itemAverages.put(itemID, newItemAverage);
} else {
itemAverage.changeDatum(prefDelta);
}
RunningAverage userAverage = userAverages.get(userID);
if (userAverage == null) {
RunningAverage newUserAveragae = new FullRunningAverage();
newUserAveragae.addDatum(prefDelta);
userAverages.put(userID, newUserAveragae);
} else {
userAverage.changeDatum(prefDelta);
}
overallAveragePrefValue.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 itemAverage = itemAverages.get(itemID);
if (itemAverage == null) {
throw new IllegalStateException("No preferences exist for item ID: " + itemID);
}
itemAverage.removeDatum(oldPref);
RunningAverage userAverage = userAverages.get(userID);
if (userAverage == null) {
throw new IllegalStateException("No preferences exist for user ID: " + userID);
}
userAverage.removeDatum(oldPref);
overallAveragePrefValue.removeDatum(oldPref);
} finally {
buildAveragesLock.writeLock().unlock();
}
}
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "ItemUserAverageRecommender";
}
private final class Estimator implements TopItems.Estimator<Long> {
private final long userID;
private Estimator(long userID) {
this.userID = userID;
}
@Override
public double estimate(Long itemID) {
return doEstimatePreference(userID, itemID);
}
}
}