/**
* 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.ItemSimilarity;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.impl.model.GenericItem;
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.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
/**
* <p>Tests {@link GenericItemBasedRecommender}.</p>
*/
public final class GenericItemBasedRecommenderTest 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(0.1, firstRecommended.getValue(), EPSILON);
recommender.refresh(null);
assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
assertEquals(0.1, firstRecommended.getValue(), EPSILON);
}
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);
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities =
new ArrayList<GenericItemSimilarity.ItemItemSimilarity>(6);
for (int i = 0; i < 6; i++) {
for (int j = i + 1; j < 6; j++) {
similarities.add(
new GenericItemSimilarity.ItemItemSimilarity(new GenericItem<String>(String.valueOf(i)),
new GenericItem<String>(String.valueOf(j)),
1.0 / (1.0 + (double) i + (double) j)));
}
}
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
Recommender recommender = new GenericItemBasedRecommender(dataModel, 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);
Item item1 = new GenericItem<String>("0");
Item item2 = new GenericItem<String>("1");
Item item3 = new GenericItem<String>("2");
Item item4 = new GenericItem<String>("3");
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities =
new ArrayList<GenericItemSimilarity.ItemItemSimilarity>(6);
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item2, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item3, 0.5));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item4, 0.2));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item3, 0.7));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item4, 0.5));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item3, item4, 0.9));
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
Recommender recommender = new GenericItemBasedRecommender(dataModel, 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(0.1, recommender.estimatePreference("test1", "2"), EPSILON);
}
/**
* Contributed test case that verifies fix for bug
* <a href="http://sourceforge.net/tracker/index.php?func=detail&aid=1396128&group_id=138771&atid=741665">
* 1396128</a>.
*/
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(0.1, firstRecommended.getValue(), EPSILON);
}
public void testMostSimilar() throws Exception {
ItemBasedRecommender recommender = buildRecommender();
List<RecommendedItem> similar = recommender.mostSimilarItems("0", 2);
assertNotNull(similar);
assertEquals(2, similar.size());
RecommendedItem first = similar.get(0);
RecommendedItem second = similar.get(1);
assertEquals("1", first.getItem().getID());
assertEquals(1.0, first.getValue(), EPSILON);
assertEquals("2", second.getItem().getID());
assertEquals(0.5, second.getValue(), EPSILON);
}
public void testMostSimilarToMultiple() throws Exception {
ItemBasedRecommender recommender = buildRecommender2();
List<Object> itemIDs = new ArrayList<Object>(2);
itemIDs.add("0");
itemIDs.add("1");
List<RecommendedItem> similar = recommender.mostSimilarItems(itemIDs, 2);
assertNotNull(similar);
assertEquals(2, similar.size());
RecommendedItem first = similar.get(0);
RecommendedItem second = similar.get(1);
assertEquals("2", first.getItem().getID());
assertEquals(0.85, first.getValue(), EPSILON);
assertEquals("3", second.getItem().getID());
assertEquals(-0.3, second.getValue(), EPSILON);
}
public void testRecommendedBecause() throws Exception {
ItemBasedRecommender recommender = buildRecommender2();
List<RecommendedItem> recommendedBecause = recommender.recommendedBecause("test1", "4", 3);
assertNotNull(recommendedBecause);
assertEquals(3, recommendedBecause.size());
RecommendedItem first = recommendedBecause.get(0);
RecommendedItem second = recommendedBecause.get(1);
RecommendedItem third = recommendedBecause.get(2);
assertEquals("2", first.getItem().getID());
assertEquals(0.99, first.getValue(), EPSILON);
assertEquals("3", second.getItem().getID());
assertEquals(0.4, second.getValue(), EPSILON);
assertEquals("0", third.getItem().getID());
assertEquals(0.2, third.getValue(), EPSILON);
}
private static ItemBasedRecommender buildRecommender() {
DataModel dataModel = new GenericDataModel(getMockUsers());
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities =
new ArrayList<GenericItemSimilarity.ItemItemSimilarity>(2);
Item item1 = new GenericItem<String>("0");
Item item2 = new GenericItem<String>("1");
Item item3 = new GenericItem<String>("2");
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item2, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item3, 0.5));
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
return new GenericItemBasedRecommender(dataModel, similarity);
}
private static ItemBasedRecommender buildRecommender2() {
List<User> users = new ArrayList<User>(4);
users.add(getUser("test1", 0.1, 0.3, 0.9, 0.8));
users.add(getUser("test2", 0.2, 0.3, 0.3, 0.4));
users.add(getUser("test3", 0.4, 0.3, 0.5, 0.1, 0.1));
users.add(getUser("test4", 0.7, 0.3, 0.8, 0.5, 0.6));
DataModel dataModel = new GenericDataModel(users);
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities =
new ArrayList<GenericItemSimilarity.ItemItemSimilarity>(10);
Item item1 = new GenericItem<String>("0");
Item item2 = new GenericItem<String>("1");
Item item3 = new GenericItem<String>("2");
Item item4 = new GenericItem<String>("3");
Item item5 = new GenericItem<String>("4");
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item2, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item3, 0.8));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item4, -0.6));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item5, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item3, 0.9));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item4, 0.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item2, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item3, item4, -0.1));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item3, item5, 0.1));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item4, item5, -0.5));
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
return new GenericItemBasedRecommender(dataModel, similarity);
}
}