/**
* 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.svd;
import org.apache.mahout.cf.taste.common.NoSuchItemException;
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.common.RandomUtils;
import org.apache.mahout.cf.taste.impl.common.RefreshHelper;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.apache.mahout.cf.taste.impl.recommender.AbstractRecommender;
import org.apache.mahout.cf.taste.impl.recommender.TopItems;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.Preference;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.recommender.Rescorer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.Random;
import java.util.concurrent.Callable;
/**
* <p>A {@link Recommender} which uses Single Value Decomposition to find the main features of the data set.
* Thanks to Simon Funk for the hints in the implementation.
*/
public final class SVDRecommender extends AbstractRecommender {
private static final Logger log = LoggerFactory.getLogger(SVDRecommender.class);
private static final Random random = RandomUtils.getRandom();
private final RefreshHelper refreshHelper;
/** Number of features */
private final int numFeatures;
private final FastByIDMap<Integer> userMap;
private final FastByIDMap<Integer> itemMap;
private final ExpectationMaximizationSVD emSvd;
private final List<Preference> cachedPreferences;
/**
* @param numFeatures the number of features
* @param initialSteps number of initial training steps
*/
public SVDRecommender(DataModel dataModel, int numFeatures, int initialSteps) throws TasteException {
super(dataModel);
this.numFeatures = numFeatures;
int numUsers = dataModel.getNumUsers();
userMap = new FastByIDMap<Integer>(numUsers);
int idx = 0;
LongPrimitiveIterator userIterator = dataModel.getUserIDs();
while (userIterator.hasNext()) {
userMap.put(userIterator.nextLong(), idx++);
}
int numItems = dataModel.getNumItems();
itemMap = new FastByIDMap<Integer>(numItems);
idx = 0;
LongPrimitiveIterator itemIterator = dataModel.getItemIDs();
while (itemIterator.hasNext()) {
itemMap.put(itemIterator.nextLong(), idx++);
}
double average = getAveragePreference();
double defaultValue = Math.sqrt((average - 1.0) / (double) numFeatures);
emSvd = new ExpectationMaximizationSVD(numUsers, numItems, numFeatures, defaultValue);
cachedPreferences = new ArrayList<Preference>(numUsers);
recachePreferences();
refreshHelper = new RefreshHelper(new Callable<Object>() {
@Override
public Object call() throws TasteException {
recachePreferences();
//TODO: train again
return null;
}
});
refreshHelper.addDependency(dataModel);
train(initialSteps);
}
private void recachePreferences() throws TasteException {
cachedPreferences.clear();
DataModel dataModel = getDataModel();
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
for (Preference pref : dataModel.getPreferencesFromUser(it.nextLong())) {
cachedPreferences.add(pref);
}
}
}
private double getAveragePreference() throws TasteException {
RunningAverage average = new FullRunningAverage();
DataModel dataModel = getDataModel();
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
for (Preference pref : dataModel.getPreferencesFromUser(it.nextLong())) {
average.addDatum(pref.getValue());
}
}
return average.getAverage();
}
public void train(int steps) {
for (int i = 0; i < steps; i++) {
nextTrainStep();
}
}
private void nextTrainStep() {
Collections.shuffle(cachedPreferences, random);
for (int i = 0; i < numFeatures; i++) {
for (Preference pref : cachedPreferences) {
int useridx = userMap.get(pref.getUserID());
int itemidx = itemMap.get(pref.getItemID());
emSvd.train(useridx, itemidx, i, pref.getValue());
}
}
}
private float predictRating(int user, int item) {
return (float) emSvd.getDotProduct(user, item);
}
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
Integer useridx = userMap.get(userID);
if (useridx == null) {
throw new NoSuchUserException();
}
Integer itemidx = itemMap.get(itemID);
if (itemidx == null) {
throw new NoSuchItemException();
}
return predictRating(useridx, itemidx);
}
@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);
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 void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "SVDRecommender[numFeatures:" + numFeatures + ']';
}
private final class Estimator implements TopItems.Estimator<Long> {
private final long theUserID;
private Estimator(long theUserID) {
this.theUserID = theUserID;
}
@Override
public double estimate(Long itemID) throws TasteException {
return estimatePreference(theUserID, itemID);
}
}
}