/**
* 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 com.google.common.collect.Lists;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.model.GenericPreference;
import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;
import org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.CandidateItemsStrategy;
import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.MostSimilarItemsCandidateItemsStrategy;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.easymock.EasyMock;
import org.junit.Test;
import java.util.Arrays;
import java.util.Collection;
import java.util.List;
/** <p>Tests {@link GenericItemBasedRecommender}.</p> */
public final class GenericItemBasedRecommenderTest extends TasteTestCase {
@Test
public void testRecommender() throws Exception {
Recommender recommender = buildRecommender();
List<RecommendedItem> recommended = recommender.recommend(1, 1);
assertNotNull(recommended);
assertEquals(1, recommended.size());
RecommendedItem firstRecommended = recommended.get(0);
assertEquals(2, firstRecommended.getItemID());
assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
recommender.refresh(null);
recommended = recommender.recommend(1, 1);
firstRecommended = recommended.get(0);
assertEquals(2, firstRecommended.getItemID());
assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
}
@Test
public void testHowMany() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4, 5},
new Double[][] {
{0.1, 0.2},
{0.2, 0.3, 0.3, 0.6},
{0.4, 0.4, 0.5, 0.9},
{0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
{0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
});
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities = Lists.newArrayList();
for (int i = 0; i < 6; i++) {
for (int j = i + 1; j < 6; j++) {
similarities.add(
new GenericItemSimilarity.ItemItemSimilarity(i, j, 1.0 / (1.0 + i + j)));
}
}
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
Recommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
}
recommender.refresh(null);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
}
}
@Test
public void testRescorer() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double[][] {
{0.1, 0.2},
{0.2, 0.3, 0.3, 0.6},
{0.4, 0.4, 0.5, 0.9},
});
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities = Lists.newArrayList();
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.5));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 3, 0.2));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.7));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.5));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, 0.9));
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
Recommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
List<RecommendedItem> originalRecommended = recommender.recommend(1, 2);
List<RecommendedItem> rescoredRecommended =
recommender.recommend(1, 2, new ReversingRescorer<Long>());
assertNotNull(originalRecommended);
assertNotNull(rescoredRecommended);
assertEquals(2, originalRecommended.size());
assertEquals(2, rescoredRecommended.size());
assertEquals(originalRecommended.get(0).getItemID(), rescoredRecommended.get(1).getItemID());
assertEquals(originalRecommended.get(1).getItemID(), rescoredRecommended.get(0).getItemID());
}
@Test
public void testEstimatePref() throws Exception {
Recommender recommender = buildRecommender();
assertEquals(0.1f, recommender.estimatePreference(1, 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>.
*/
@Test
public void testBestRating() throws Exception {
Recommender recommender = buildRecommender();
List<RecommendedItem> recommended = recommender.recommend(1, 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(2, firstRecommended.getItemID());
assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
}
@Test
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.getItemID());
assertEquals(1.0f, first.getValue(), EPSILON);
assertEquals(2, second.getItemID());
assertEquals(0.5f, second.getValue(), EPSILON);
}
@Test
public void testMostSimilarToMultiple() throws Exception {
ItemBasedRecommender recommender = buildRecommender2();
List<RecommendedItem> similar = recommender.mostSimilarItems(new long[] {0, 1}, 2);
assertNotNull(similar);
assertEquals(2, similar.size());
RecommendedItem first = similar.get(0);
RecommendedItem second = similar.get(1);
assertEquals(2, first.getItemID());
assertEquals(0.85f, first.getValue(), EPSILON);
assertEquals(3, second.getItemID());
assertEquals(-0.3f, second.getValue(), EPSILON);
}
@Test
public void testMostSimilarToMultipleExcludeIfNotSimilarToAll() throws Exception {
ItemBasedRecommender recommender = buildRecommender2();
List<RecommendedItem> similar = recommender.mostSimilarItems(new long[] {3, 4}, 2);
assertNotNull(similar);
assertEquals(1, similar.size());
RecommendedItem first = similar.get(0);
assertEquals(0, first.getItemID());
assertEquals(0.2f, first.getValue(), EPSILON);
}
@Test
public void testMostSimilarToMultipleDontExcludeIfNotSimilarToAll() throws Exception {
ItemBasedRecommender recommender = buildRecommender2();
List<RecommendedItem> similar = recommender.mostSimilarItems(new long[] {1, 2, 4}, 10, false);
assertNotNull(similar);
assertEquals(2, similar.size());
RecommendedItem first = similar.get(0);
RecommendedItem second = similar.get(1);
assertEquals(0, first.getItemID());
assertEquals(0.933333333f, first.getValue(), EPSILON);
assertEquals(3, second.getItemID());
assertEquals(-0.2f, second.getValue(), EPSILON);
}
@Test
public void testRecommendedBecause() throws Exception {
ItemBasedRecommender recommender = buildRecommender2();
List<RecommendedItem> recommendedBecause = recommender.recommendedBecause(1, 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.getItemID());
assertEquals(0.99f, first.getValue(), EPSILON);
assertEquals(3, second.getItemID());
assertEquals(0.4f, second.getValue(), EPSILON);
assertEquals(0, third.getItemID());
assertEquals(0.2f, third.getValue(), EPSILON);
}
private static ItemBasedRecommender buildRecommender() {
DataModel dataModel = getDataModel();
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities = Lists.newArrayList();
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.5));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.0));
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
return new GenericItemBasedRecommender(dataModel, similarity);
}
private static ItemBasedRecommender buildRecommender2() {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4},
new Double[][] {
{0.1, 0.3, 0.9, 0.8},
{0.2, 0.3, 0.3, 0.4},
{0.4, 0.3, 0.5, 0.1, 0.1},
{0.7, 0.3, 0.8, 0.5, 0.6},
});
Collection<GenericItemSimilarity.ItemItemSimilarity> similarities = Lists.newArrayList();
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.8));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 3, -0.6));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 4, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.9));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 1, 1.0));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, -0.1));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(2, 4, 0.1));
similarities.add(new GenericItemSimilarity.ItemItemSimilarity(3, 4, -0.5));
ItemSimilarity similarity = new GenericItemSimilarity(similarities);
return new GenericItemBasedRecommender(dataModel, similarity);
}
/**
* we're making sure that a user's preferences are fetched only once from the {@link DataModel} for one call to
* {@link GenericItemBasedRecommender#recommend(long, int)}
*
* @throws Exception
*/
@Test
public void preferencesFetchedOnlyOnce() throws Exception {
DataModel dataModel = EasyMock.createMock(DataModel.class);
ItemSimilarity itemSimilarity = EasyMock.createMock(ItemSimilarity.class);
CandidateItemsStrategy candidateItemsStrategy = EasyMock.createMock(CandidateItemsStrategy.class);
MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy =
EasyMock.createMock(MostSimilarItemsCandidateItemsStrategy.class);
PreferenceArray preferencesFromUser = new GenericUserPreferenceArray(
Arrays.asList(new GenericPreference(1L, 1L, 5.0f), new GenericPreference(1L, 2L, 4.0f)));
EasyMock.expect(dataModel.getMinPreference()).andReturn(Float.NaN);
EasyMock.expect(dataModel.getMaxPreference()).andReturn(Float.NaN);
EasyMock.expect(dataModel.getPreferencesFromUser(1L)).andReturn(preferencesFromUser);
EasyMock.expect(candidateItemsStrategy.getCandidateItems(1L, preferencesFromUser, dataModel))
.andReturn(new FastIDSet(new long[] { 3L, 4L }));
EasyMock.expect(itemSimilarity.itemSimilarities(3L, preferencesFromUser.getIDs()))
.andReturn(new double[] { 0.5, 0.3 });
EasyMock.expect(itemSimilarity.itemSimilarities(4L, preferencesFromUser.getIDs()))
.andReturn(new double[] { 0.4, 0.1 });
EasyMock.replay(dataModel, itemSimilarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);
Recommender recommender = new GenericItemBasedRecommender(dataModel, itemSimilarity,
candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);
recommender.recommend(1L, 3);
EasyMock.verify(dataModel, itemSimilarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);
}
}