/**
* 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.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.FullRunningAverage;
import org.apache.mahout.common.LongPair;
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.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Rescorer;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
/**
* <p>A simple {@link org.apache.mahout.cf.taste.recommender.Recommender} which uses a given {@link
* org.apache.mahout.cf.taste.model.DataModel} and {@link org.apache.mahout.cf.taste.similarity.ItemSimilarity} to
* produce recommendations. This class represents Taste's support for item-based recommenders.</p>
*
* <p>The {@link org.apache.mahout.cf.taste.similarity.ItemSimilarity} is the most important point to discuss here.
* Item-based recommenders are useful because they can take advantage of something to be very fast: they base their
* computations on item similarity, not user similarity, and item similarity is relatively static. It can be
* precomputed, instead of re-computed in real time.</p>
*
* <p>Thus it's strongly recommended that you use {@link org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity}
* with pre-computed similarities if you're going to use this class. You can use {@link
* org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity} too, which computes similarities in
* real-time, but will probably find this painfully slow for large amounts of data.</p>
*/
public class GenericItemBasedRecommender extends AbstractRecommender implements ItemBasedRecommender {
private static final Logger log = LoggerFactory.getLogger(GenericItemBasedRecommender.class);
private final ItemSimilarity similarity;
private final RefreshHelper refreshHelper;
public GenericItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity) {
super(dataModel);
if (similarity == null) {
throw new IllegalArgumentException("similarity is null");
}
this.similarity = similarity;
this.refreshHelper = new RefreshHelper(null);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(similarity);
}
public ItemSimilarity getSimilarity() {
return similarity;
}
@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);
if (getNumPreferences(userID) == 0) {
return Collections.emptyList();
}
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 model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
return doEstimatePreference(userID, itemID);
}
@Override
public List<RecommendedItem> mostSimilarItems(long itemID, int howMany) throws TasteException {
return mostSimilarItems(itemID, howMany, null);
}
@Override
public List<RecommendedItem> mostSimilarItems(long itemID,
int howMany,
Rescorer<LongPair> rescorer) throws TasteException {
TopItems.Estimator<Long> estimator = new MostSimilarEstimator(itemID, similarity, rescorer);
return doMostSimilarItems(new long[] {itemID}, howMany, estimator);
}
@Override
public List<RecommendedItem> mostSimilarItems(long[] itemIDs, int howMany) throws TasteException {
return mostSimilarItems(itemIDs, howMany, null);
}
@Override
public List<RecommendedItem> mostSimilarItems(long[] itemIDs,
int howMany,
Rescorer<LongPair> rescorer) throws TasteException {
TopItems.Estimator<Long> estimator = new MultiMostSimilarEstimator(itemIDs, similarity, rescorer);
return doMostSimilarItems(itemIDs, howMany, estimator);
}
@Override
public List<RecommendedItem> recommendedBecause(long userID,
long itemID,
int howMany) throws TasteException {
if (howMany < 1) {
throw new IllegalArgumentException("howMany must be at least 1");
}
DataModel model = getDataModel();
TopItems.Estimator<Long> estimator = new RecommendedBecauseEstimator(userID, itemID, similarity);
PreferenceArray prefs = model.getPreferencesFromUser(userID);
int size = prefs.length();
FastIDSet allUserItems = new FastIDSet(size);
for (int i = 0; i < size; i++) {
allUserItems.add(prefs.getItemID(i));
}
allUserItems.remove(itemID);
return TopItems.getTopItems(howMany, allUserItems.iterator(), null, estimator);
}
private List<RecommendedItem> doMostSimilarItems(long[] itemIDs,
int howMany,
TopItems.Estimator<Long> estimator) throws TasteException {
DataModel model = getDataModel();
FastIDSet possibleItemsIDs = new FastIDSet();
for (long itemID : itemIDs) {
PreferenceArray prefs = model.getPreferencesForItem(itemID);
int size = prefs.length();
for (int i = 0; i < size; i++) {
long userID = prefs.get(i).getUserID();
possibleItemsIDs.addAll(model.getItemIDsFromUser(userID));
}
}
possibleItemsIDs.removeAll(itemIDs);
return TopItems.getTopItems(howMany, possibleItemsIDs.iterator(), null, estimator);
}
protected float doEstimatePreference(long userID, long itemID) throws TasteException {
double preference = 0.0;
double totalSimilarity = 0.0;
int count = 0;
PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
int size = prefs.length();
for (int i = 0; i < size; i++) {
double theSimilarity = similarity.itemSimilarity(itemID, prefs.getItemID(i));
if (!Double.isNaN(theSimilarity)) {
// Why + 1.0? similarity ranges from -1.0 to 1.0, and we want to use it as a simple
// weight. To avoid negative values, we add 1.0 to put it in
// the [0.0,2.0] range which is reasonable for weights
theSimilarity += 1.0;
preference += theSimilarity * prefs.getValue(i);
totalSimilarity += theSimilarity;
count++;
}
}
// Throw out the estimate if it was based on no data points, of course, but also if based on
// just one. This is a bit of a band-aid on the 'stock' item-based algorithm for the moment.
// The reason is that in this case the estimate is, simply, the user's rating for one item
// that happened to have a defined similarity. The similarity score doesn't matter, and that
// seems like a bad situation.
return count <= 1 ? Float.NaN : (float) (preference / totalSimilarity);
}
private int getNumPreferences(long userID) throws TasteException {
return getDataModel().getPreferencesFromUser(userID).length();
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "GenericItemBasedRecommender[similarity:" + similarity + ']';
}
public static class MostSimilarEstimator implements TopItems.Estimator<Long> {
private final long toItemID;
private final ItemSimilarity similarity;
private final Rescorer<LongPair> rescorer;
public MostSimilarEstimator(long toItemID,
ItemSimilarity similarity,
Rescorer<LongPair> rescorer) {
this.toItemID = toItemID;
this.similarity = similarity;
this.rescorer = rescorer;
}
@Override
public double estimate(Long itemID) throws TasteException {
LongPair pair = new LongPair(toItemID, itemID);
if (rescorer != null && rescorer.isFiltered(pair)) {
return Double.NaN;
}
double originalEstimate = similarity.itemSimilarity(toItemID, itemID);
return rescorer == null ? originalEstimate : rescorer.rescore(pair, originalEstimate);
}
}
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) throws TasteException {
return doEstimatePreference(userID, itemID);
}
}
private static class MultiMostSimilarEstimator implements TopItems.Estimator<Long> {
private final long[] toItemIDs;
private final ItemSimilarity similarity;
private final Rescorer<LongPair> rescorer;
private MultiMostSimilarEstimator(long[] toItemIDs,
ItemSimilarity similarity,
Rescorer<LongPair> rescorer) {
this.toItemIDs = toItemIDs;
this.similarity = similarity;
this.rescorer = rescorer;
}
@Override
public double estimate(Long itemID) throws TasteException {
RunningAverage average = new FullRunningAverage();
for (long toItemID : toItemIDs) {
LongPair pair = new LongPair(toItemID, itemID);
if (rescorer != null && rescorer.isFiltered(pair)) {
continue;
}
double estimate = similarity.itemSimilarity(toItemID, itemID);
if (rescorer != null) {
estimate = rescorer.rescore(pair, estimate);
}
average.addDatum(estimate);
}
return average.getAverage();
}
}
private class RecommendedBecauseEstimator implements TopItems.Estimator<Long> {
private final long userID;
private final long recommendedItemID;
private final ItemSimilarity similarity;
private RecommendedBecauseEstimator(long userID,
long recommendedItemID,
ItemSimilarity similarity) {
this.userID = userID;
this.recommendedItemID = recommendedItemID;
this.similarity = similarity;
}
@Override
public double estimate(Long itemID) throws TasteException {
Float pref = getDataModel().getPreferenceValue(userID, itemID);
if (pref == null) {
return Float.NaN;
}
double similarityValue = similarity.itemSimilarity(recommendedItemID, itemID);
return (1.0 + similarityValue) * pref;
}
}
}