/**
* 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.Collections;
import java.util.List;
import java.util.Random;
import java.util.concurrent.Callable;
import com.google.common.collect.Lists;
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.recommender.ClusteringRecommender;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.common.Pair;
import org.apache.mahout.common.RandomUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.google.common.base.Preconditions;
/**
* <p>
* A {@link org.apache.mahout.cf.taste.recommender.Recommender} that clusters users, then determines the
* clusters' top recommendations. This implementation builds clusters by repeatedly merging clusters until
* only a certain number remain, meaning that each cluster is sort of a tree of other clusters.
* </p>
*
* <p>
* This {@link org.apache.mahout.cf.taste.recommender.Recommender} therefore has a few properties to note:
* </p>
*
* <ul>
* <li>For all users in a cluster, recommendations will be the same</li>
* <li>{@link #estimatePreference(long, long)} may well return {@link Double#NaN}; it does so when asked to
* estimate preference for an item for which no preference is expressed in the users in the cluster.</li>
* </ul>
*/
public final class TreeClusteringRecommender extends AbstractRecommender implements ClusteringRecommender {
private static final Logger log = LoggerFactory.getLogger(TreeClusteringRecommender.class);
private static final FastIDSet[] NO_CLUSTERS = new FastIDSet[0];
private final Random random;
private final ClusterSimilarity clusterSimilarity;
private final int numClusters;
private final double clusteringThreshold;
private final boolean clusteringByThreshold;
private final double samplingRate;
private FastByIDMap<List<RecommendedItem>> topRecsByUserID;
private FastIDSet[] allClusters;
private FastByIDMap<FastIDSet> clustersByUserID;
private final RefreshHelper refreshHelper;
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param numClusters
* desired number of clusters to create
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code numClusters} is less than 2
*/
public TreeClusteringRecommender(DataModel dataModel, ClusterSimilarity clusterSimilarity, int numClusters)
throws TasteException {
this(dataModel, clusterSimilarity, numClusters, 1.0);
}
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param numClusters
* desired number of clusters to create
* @param samplingRate
* percentage of all cluster-cluster pairs to consider when finding next-most-similar clusters.
* Decreasing this value from 1.0 can increase performance at the cost of accuracy
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code numClusters} is less than 2, or samplingRate
* is {@link Double#NaN} or nonpositive or greater than 1.0
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
int numClusters,
double samplingRate) throws TasteException {
super(dataModel);
Preconditions.checkArgument(numClusters >= 2, "numClusters must be at least 2");
Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
"samplingRate is invalid: %f", samplingRate);
random = RandomUtils.getRandom();
this.clusterSimilarity = Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = numClusters;
this.clusteringThreshold = Double.NaN;
this.clusteringByThreshold = false;
this.samplingRate = samplingRate;
this.refreshHelper = new RefreshHelper(new Callable<Object>() {
@Override
public Object call() throws TasteException {
buildClusters();
return null;
}
});
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param clusteringThreshold
* clustering similarity threshold; clusters will be aggregated into larger clusters until the next
* two nearest clusters' similarity drops below this threshold
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code clusteringThreshold} is {@link Double#NaN}
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold) throws TasteException {
this(dataModel, clusterSimilarity, clusteringThreshold, 1.0);
}
/**
* @param dataModel
* {@link DataModel} which provides users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param clusteringThreshold
* clustering similarity threshold; clusters will be aggregated into larger clusters until the next
* two nearest clusters' similarity drops below this threshold
* @param samplingRate
* percentage of all cluster-cluster pairs to consider when finding next-most-similar clusters.
* Decreasing this value from 1.0 can increase performance at the cost of accuracy
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code clusteringThreshold} is {@link Double#NaN},
* or samplingRate is {@link Double#NaN} or nonpositive or greater than 1.0
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold,
double samplingRate) throws TasteException {
super(dataModel);
Preconditions.checkArgument(!Double.isNaN(clusteringThreshold), "clusteringThreshold must not be NaN");
Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0, "samplingRate is invalid: %f", samplingRate);
random = RandomUtils.getRandom();
this.clusterSimilarity = Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = Integer.MIN_VALUE;
this.clusteringThreshold = clusteringThreshold;
this.clusteringByThreshold = true;
this.samplingRate = samplingRate;
this.refreshHelper = new RefreshHelper(new Callable<Object>() {
@Override
public Object call() throws TasteException {
buildClusters();
return null;
}
});
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
buildClusters();
log.debug("Recommending items for user ID '{}'", userID);
List<RecommendedItem> recommended = topRecsByUserID.get(userID);
if (recommended == null) {
return Collections.emptyList();
}
DataModel dataModel = getDataModel();
List<RecommendedItem> rescored = Lists.newArrayListWithCapacity(recommended.size());
// Only add items the user doesn't already have a preference for.
// And that the rescorer doesn't "reject".
for (RecommendedItem recommendedItem : recommended) {
long itemID = recommendedItem.getItemID();
if (rescorer != null && rescorer.isFiltered(itemID)) {
continue;
}
if (dataModel.getPreferenceValue(userID, itemID) == null
&& (rescorer == null || !Double.isNaN(rescorer.rescore(itemID, recommendedItem.getValue())))) {
rescored.add(recommendedItem);
}
}
Collections.sort(rescored, new ByRescoreComparator(rescorer));
return rescored;
}
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
DataModel model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
buildClusters();
List<RecommendedItem> topRecsForUser = topRecsByUserID.get(userID);
if (topRecsForUser != null) {
for (RecommendedItem item : topRecsForUser) {
if (itemID == item.getItemID()) {
return item.getValue();
}
}
}
// Hmm, we have no idea. The item is not in the user's cluster
return Float.NaN;
}
@Override
public FastIDSet getCluster(long userID) throws TasteException {
buildClusters();
FastIDSet cluster = clustersByUserID.get(userID);
return cluster == null ? new FastIDSet() : cluster;
}
@Override
public FastIDSet[] getClusters() throws TasteException {
buildClusters();
return allClusters;
}
private void buildClusters() throws TasteException {
DataModel model = getDataModel();
int numUsers = model.getNumUsers();
if (numUsers > 0) {
List<FastIDSet> newClusters = Lists.newArrayListWithCapacity(numUsers);
// Begin with a cluster for each user:
LongPrimitiveIterator it = model.getUserIDs();
while (it.hasNext()) {
FastIDSet newCluster = new FastIDSet();
newCluster.add(it.nextLong());
newClusters.add(newCluster);
}
if (numUsers > 1) {
findClusters(newClusters);
}
topRecsByUserID = computeTopRecsPerUserID(newClusters);
clustersByUserID = computeClustersPerUserID(newClusters);
allClusters = newClusters.toArray(new FastIDSet[newClusters.size()]);
} else {
topRecsByUserID = new FastByIDMap<List<RecommendedItem>>();
clustersByUserID = new FastByIDMap<FastIDSet>();
allClusters = NO_CLUSTERS;
}
}
private void findClusters(List<FastIDSet> newClusters) throws TasteException {
if (clusteringByThreshold) {
Pair<FastIDSet,FastIDSet> nearestPair = findNearestClusters(newClusters);
if (nearestPair != null) {
FastIDSet cluster1 = nearestPair.getFirst();
FastIDSet cluster2 = nearestPair.getSecond();
while (clusterSimilarity.getSimilarity(cluster1, cluster2) >= clusteringThreshold) {
newClusters.remove(cluster1);
newClusters.remove(cluster2);
FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
merged.addAll(cluster1);
merged.addAll(cluster2);
newClusters.add(merged);
nearestPair = findNearestClusters(newClusters);
if (nearestPair == null) {
break;
}
cluster1 = nearestPair.getFirst();
cluster2 = nearestPair.getSecond();
}
}
} else {
while (newClusters.size() > numClusters) {
Pair<FastIDSet,FastIDSet> nearestPair = findNearestClusters(newClusters);
if (nearestPair == null) {
break;
}
FastIDSet cluster1 = nearestPair.getFirst();
FastIDSet cluster2 = nearestPair.getSecond();
newClusters.remove(cluster1);
newClusters.remove(cluster2);
FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
merged.addAll(cluster1);
merged.addAll(cluster2);
newClusters.add(merged);
}
}
}
private Pair<FastIDSet,FastIDSet> findNearestClusters(List<FastIDSet> clusters) throws TasteException {
int size = clusters.size();
Pair<FastIDSet,FastIDSet> nearestPair = null;
double bestSimilarity = Double.NEGATIVE_INFINITY;
for (int i = 0; i < size; i++) {
FastIDSet cluster1 = clusters.get(i);
for (int j = i + 1; j < size; j++) {
if (samplingRate >= 1.0 || random.nextDouble() < samplingRate) {
FastIDSet cluster2 = clusters.get(j);
double similarity = clusterSimilarity.getSimilarity(cluster1, cluster2);
if (!Double.isNaN(similarity) && similarity > bestSimilarity) {
bestSimilarity = similarity;
nearestPair = new Pair<FastIDSet,FastIDSet>(cluster1, cluster2);
}
}
}
}
return nearestPair;
}
private FastByIDMap<List<RecommendedItem>> computeTopRecsPerUserID(Iterable<FastIDSet> clusters)
throws TasteException {
FastByIDMap<List<RecommendedItem>> recsPerUser = new FastByIDMap<List<RecommendedItem>>();
for (FastIDSet cluster : clusters) {
List<RecommendedItem> recs = computeTopRecsForCluster(cluster);
LongPrimitiveIterator it = cluster.iterator();
while (it.hasNext()) {
recsPerUser.put(it.nextLong(), recs);
}
}
return recsPerUser;
}
private List<RecommendedItem> computeTopRecsForCluster(FastIDSet cluster) throws TasteException {
DataModel dataModel = getDataModel();
FastIDSet possibleItemIDs = new FastIDSet();
LongPrimitiveIterator it = cluster.iterator();
while (it.hasNext()) {
possibleItemIDs.addAll(dataModel.getItemIDsFromUser(it.nextLong()));
}
TopItems.Estimator<Long> estimator = new Estimator(cluster);
List<RecommendedItem> topItems =
TopItems.getTopItems(possibleItemIDs.size(), possibleItemIDs.iterator(), null, estimator);
log.debug("Recommendations are: {}", topItems);
return Collections.unmodifiableList(topItems);
}
private static FastByIDMap<FastIDSet> computeClustersPerUserID(Collection<FastIDSet> clusters) {
FastByIDMap<FastIDSet> clustersPerUser = new FastByIDMap<FastIDSet>(clusters.size());
for (FastIDSet cluster : clusters) {
LongPrimitiveIterator it = cluster.iterator();
while (it.hasNext()) {
clustersPerUser.put(it.nextLong(), cluster);
}
}
return clustersPerUser;
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "TreeClusteringRecommender[clusterSimilarity:" + clusterSimilarity + ']';
}
private final class Estimator implements TopItems.Estimator<Long> {
private final FastIDSet cluster;
private Estimator(FastIDSet cluster) {
this.cluster = cluster;
}
@Override
public double estimate(Long itemID) throws TasteException {
DataModel dataModel = getDataModel();
RunningAverage average = new FullRunningAverage();
LongPrimitiveIterator it = cluster.iterator();
while (it.hasNext()) {
Float pref = dataModel.getPreferenceValue(it.nextLong(), itemID);
if (pref != null) {
average.addDatum(pref);
}
}
return average.getAverage();
}
}
}