/**
* 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.similarity.UserSimilarity;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.impl.model.GenericItem;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.Item;
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.UserBasedRecommender;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
/**
* <p>Tests {@link GenericUserBasedRecommender}.</p>
*/
public final class GenericUserBasedRecommenderTest extends TasteTestCase {
public void testRecommender() throws Exception {
Recommender recommender = buildRecommender();
List<RecommendedItem> recommended = recommender.recommend("test1", 1);
assertNotNull(recommended);
assertEquals(1, recommended.size());
RecommendedItem firstRecommended = recommended.get(0);
assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
assertEquals(0.3, firstRecommended.getValue());
recommender.refresh(null);
assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
assertEquals(0.3, firstRecommended.getValue());
}
public void testHowMany() throws Exception {
List<User> users = new ArrayList<User>(3);
users.add(getUser("test1", 0.1, 0.2));
users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
users.add(getUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
users.add(getUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
DataModel dataModel = new GenericDataModel(users);
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItem(), moreRecommended.get(i).getItem());
}
recommender.refresh(null);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItem(), moreRecommended.get(i).getItem());
}
}
public void testRescorer() throws Exception {
List<User> users = new ArrayList<User>(3);
users.add(getUser("test1", 0.1, 0.2));
users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
DataModel dataModel = new GenericDataModel(users);
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
List<RecommendedItem> originalRecommended = recommender.recommend("test1", 2);
List<RecommendedItem> rescoredRecommended =
recommender.recommend("test1", 2, new ReversingRescorer<Item>());
assertNotNull(originalRecommended);
assertNotNull(rescoredRecommended);
assertEquals(2, originalRecommended.size());
assertEquals(2, rescoredRecommended.size());
assertEquals(originalRecommended.get(0).getItem(), rescoredRecommended.get(1).getItem());
assertEquals(originalRecommended.get(1).getItem(), rescoredRecommended.get(0).getItem());
}
public void testEstimatePref() throws Exception {
Recommender recommender = buildRecommender();
assertEquals(0.3, recommender.estimatePreference("test1", "2"));
}
public void testBestRating() throws Exception {
Recommender recommender = buildRecommender();
List<RecommendedItem> recommended = recommender.recommend("test1", 1);
assertNotNull(recommended);
assertEquals(1, recommended.size());
RecommendedItem firstRecommended = recommended.get(0);
// item one should be recommended because it has a greater rating/score
assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
assertEquals(0.3, firstRecommended.getValue(), EPSILON);
}
public void testMostSimilar() throws Exception {
UserBasedRecommender recommender = buildRecommender();
List<User> similar = recommender.mostSimilarUsers("test1", 2);
assertNotNull(similar);
assertEquals(2, similar.size());
User first = similar.get(0);
User second = similar.get(1);
assertEquals("test2", first.getID());
assertEquals("test3", second.getID());
}
public void testIsolatedUser() throws Exception {
List<User> users = new ArrayList<User>(3);
users.add(getUser("test1", 0.1, 0.2));
users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
users.add(getUser("test4"));
DataModel dataModel = new GenericDataModel(users);
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel);
UserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
Collection<User> mostSimilar = recommender.mostSimilarUsers("test4", 3);
assertNotNull(mostSimilar);
assertEquals(0, mostSimilar.size());
}
private static UserBasedRecommender buildRecommender() throws Exception {
DataModel dataModel = new GenericDataModel(getMockUsers());
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
return new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
}
}