/**
* 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.similarity.UserSimilarity;
import org.apache.mahout.cf.taste.impl.common.Pair;
import org.apache.mahout.cf.taste.impl.common.RefreshHelper;
import org.apache.mahout.cf.taste.impl.common.FastSet;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.Item;
import org.apache.mahout.cf.taste.model.Preference;
import org.apache.mahout.cf.taste.model.User;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
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.apache.mahout.cf.taste.recommender.UserBasedRecommender;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.Set;
/**
* <p>A simple {@link Recommender} which uses a given {@link DataModel} and {@link UserNeighborhood}
* to produce recommendations.</p>
*/
public final class GenericUserBasedRecommender extends AbstractRecommender implements UserBasedRecommender {
private static final Logger log = LoggerFactory.getLogger(GenericUserBasedRecommender.class);
private final UserNeighborhood neighborhood;
private final UserSimilarity similarity;
private final RefreshHelper refreshHelper;
public GenericUserBasedRecommender(DataModel dataModel,
UserNeighborhood neighborhood,
UserSimilarity similarity) {
super(dataModel);
if (neighborhood == null) {
throw new IllegalArgumentException("neighborhood is null");
}
this.neighborhood = neighborhood;
this.similarity = similarity;
this.refreshHelper = new RefreshHelper(null);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(similarity);
refreshHelper.addDependency(neighborhood);
}
@Override
public List<RecommendedItem> recommend(Object userID, int howMany, Rescorer<Item> rescorer)
throws TasteException {
if (userID == null) {
throw new IllegalArgumentException("userID is null");
}
if (howMany < 1) {
throw new IllegalArgumentException("howMany must be at least 1");
}
log.debug("Recommending items for user ID '{}'", userID);
User theUser = getDataModel().getUser(userID);
Collection<User> theNeighborhood = neighborhood.getUserNeighborhood(userID);
log.trace("UserNeighborhood is: {}", neighborhood);
if (theNeighborhood.isEmpty()) {
return Collections.emptyList();
}
Set<Item> allItems = getAllOtherItems(theNeighborhood, theUser);
log.trace("Items in neighborhood which user doesn't prefer already are: {}", allItems);
TopItems.Estimator<Item> estimator = new Estimator(theUser, theNeighborhood);
List<RecommendedItem> topItems = TopItems.getTopItems(howMany, allItems, rescorer, estimator);
log.debug("Recommendations are: {}", topItems);
return topItems;
}
@Override
public double estimatePreference(Object userID, Object itemID) throws TasteException {
DataModel model = getDataModel();
User theUser = model.getUser(userID);
Preference actualPref = theUser.getPreferenceFor(itemID);
if (actualPref != null) {
return actualPref.getValue();
}
Collection<User> theNeighborhood = neighborhood.getUserNeighborhood(userID);
Item item = model.getItem(itemID);
return doEstimatePreference(theUser, theNeighborhood, item);
}
@Override
public List<User> mostSimilarUsers(Object userID, int howMany) throws TasteException {
return mostSimilarUsers(userID, howMany, null);
}
@Override
public List<User> mostSimilarUsers(Object userID,
int howMany,
Rescorer<Pair<User, User>> rescorer) throws TasteException {
User toUser = getDataModel().getUser(userID);
TopItems.Estimator<User> estimator = new MostSimilarEstimator(toUser, similarity, rescorer);
return doMostSimilarUsers(howMany, estimator);
}
private List<User> doMostSimilarUsers(int howMany,
TopItems.Estimator<User> estimator) throws TasteException {
DataModel model = getDataModel();
return TopItems.getTopUsers(howMany, model.getUsers(), null, estimator);
}
private double doEstimatePreference(User theUser, Collection<User> theNeighborhood, Item item)
throws TasteException {
if (theNeighborhood.isEmpty()) {
return Double.NaN;
}
double preference = 0.0;
double totalSimilarity = 0.0;
for (User user : theNeighborhood) {
if (!user.equals(theUser)) {
// See GenericItemBasedRecommender.doEstimatePreference() too
Preference pref = user.getPreferenceFor(item.getID());
if (pref != null) {
double theSimilarity = similarity.userSimilarity(theUser, user) + 1.0;
if (!Double.isNaN(theSimilarity)) {
preference += theSimilarity * pref.getValue();
totalSimilarity += theSimilarity;
}
}
}
}
return totalSimilarity == 0.0 ? Double.NaN : preference / totalSimilarity;
}
private static Set<Item> getAllOtherItems(Iterable<User> theNeighborhood, User theUser) {
Set<Item> allItems = new FastSet<Item>();
for (User user : theNeighborhood) {
Preference[] prefs = user.getPreferencesAsArray();
for (Preference pref : prefs) {
Item item = pref.getItem();
// If not already preferred by the user, add it
if (theUser.getPreferenceFor(item.getID()) == null) {
allItems.add(item);
}
}
}
return allItems;
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "GenericUserBasedRecommender[neighborhood:" + neighborhood + ']';
}
private static class MostSimilarEstimator implements TopItems.Estimator<User> {
private final User toUser;
private final UserSimilarity similarity;
private final Rescorer<Pair<User, User>> rescorer;
private MostSimilarEstimator(User toUser,
UserSimilarity similarity,
Rescorer<Pair<User, User>> rescorer) {
this.toUser = toUser;
this.similarity = similarity;
this.rescorer = rescorer;
}
@Override
public double estimate(User user) throws TasteException {
// Don't consider the user itself as a possible most similar user
if (user.equals(toUser)) {
return Double.NaN;
}
Pair<User, User> pair = new Pair<User, User>(toUser, user);
if (rescorer != null && rescorer.isFiltered(pair)) {
return Double.NaN;
}
double originalEstimate = similarity.userSimilarity(toUser, user);
return rescorer == null ? originalEstimate : rescorer.rescore(pair, originalEstimate);
}
}
private final class Estimator implements TopItems.Estimator<Item> {
private final User theUser;
private final Collection<User> theNeighborhood;
Estimator(User theUser, Collection<User> theNeighborhood) {
this.theUser = theUser;
this.theNeighborhood = theNeighborhood;
}
@Override
public double estimate(Item item) throws TasteException {
return doEstimatePreference(theUser, theNeighborhood, item);
}
}
}