Package org.apache.mahout.cf.taste.recommender

Examples of org.apache.mahout.cf.taste.recommender.Recommender

115
116
117
118
119
120
121
122
123
124
125
126
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    users.add(getUser("test4", 0.7, 0.3, 0.8));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    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());
View Full Code Here
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
* <p>Tests {@link SlopeOneRecommender}.</p>
*/
public final class SlopeOneRecommenderTest 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(0.34803885284992736, firstRecommended.getValue(), EPSILON);
    recommender.refresh(null);
    assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
    assertEquals(0.34803885284992736, firstRecommended.getValue(), EPSILON);
  }
View Full Code Here
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
    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);
    Recommender recommender = new SlopeOneRecommender(dataModel);
    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());
    }
  }
View Full Code Here
73
74
75
76
77
78
79
80
81
82
83
84
85
86
    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);
    Recommender recommender = new SlopeOneRecommender(dataModel);
    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());
View Full Code Here
86
87
88
89
90
91
92
93
    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(0.34803885284992736, recommender.estimatePreference("test1", "2"), EPSILON);
  }
View Full Code Here
96
97
98
99
100
101
102
103
104
105
106
107
    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.0, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    DataModel dataModel = new GenericDataModel(users);
    Recommender recommender = new SlopeOneRecommender(dataModel);
    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());
View Full Code Here
111
112
113
114
115
116
117
118
  public void testDiffStdevBehavior() 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, 0.6));
    DataModel dataModel = new GenericDataModel(users);
    Recommender recommender = new SlopeOneRecommender(dataModel);
    assertEquals(0.6, recommender.estimatePreference("test1", "2"), EPSILON);
  }
View Full Code Here
99
100
101
102
103
104
105
106
107
108
109
110
111
112
    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());
View Full Code Here
112
113
114
115
116
117
118
119
    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);
  }
View Full Code Here
122
123
124
125
126
127
128
129
130
131
132
133
   * Contributed test case that verifies fix for bug
   * <a href="http://sourceforge.net/tracker/index.php?func=detail&amp;aid=1396128&amp;group_id=138771&amp;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());
View Full Code Here
TOP

Related Classes of org.apache.mahout.cf.taste.recommender.Recommender

  • com.manning.hip.ch9.MovieUserRecommender
  • mia.recommender.ch04.GroupLensDataModelIntro
  • mia.recommender.ch05.LibimsetiLoadRunner
  • org.apache.mahout.cf.taste.example.kddcup.track1.Track1RecommenderEvaluator
  • org.apache.mahout.cf.taste.impl.eval.AbstractDifferenceRecommenderEvaluator
  • org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator
  • org.apache.mahout.cf.taste.impl.eval.LoadEvaluationRunner
  • org.apache.mahout.cf.taste.impl.LoadTest
  • org.apache.mahout.cf.taste.impl.model.file.FileDataModelTest
  • org.apache.mahout.cf.taste.impl.recommender.CachingRecommenderTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.