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

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

79
80
81
82
83
84
85
86
87
88
89
90
91
92
                    {0.2, 0.3, 0.3, 0.6},
                    {0.4, 0.5, 0.5, 0.9},
            });
    ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    Recommender recommender = new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
    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());
View Full Code Here
93
94
95
96
97
98
99
100
    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);
  }
View Full Code Here
99
100
101
102
103
104
105
106
107
108
109
110
    assertEquals(0.1f, recommender.estimatePreference(1, 2), EPSILON);
  }

  @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());
View Full Code Here
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
/** <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);
  }
View Full Code Here
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
        similarities.add(
            new GenericItemSimilarity.ItemItemSimilarity(i, 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(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());
    }
  }
View Full Code Here
101
102
103
104
105
106
107
108
109
110
111
112
113
114
    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());
View Full Code Here
115
116
117
118
119
120
121
122
    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);
  }
View Full Code Here
126
127
128
129
130
131
132
133
134
135
136
137
   * <a href="http://sourceforge.net/tracker/index.php?func=detail&amp;aid=1396128&amp;group_id=138771&amp;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());
View Full Code Here
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
/** <p>Tests {@link SlopeOneRecommender}.</p> */
public final class SlopeOneRecommenderTest 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(0.34803885284992736, firstRecommended.getValue(), EPSILON);
    recommender.refresh(null);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.34803885284992736, firstRecommended.getValue(), EPSILON);
  }
View Full Code Here
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
                    {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},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    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());
    }
  }
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.