Package org.apache.mahout.cf.taste.impl.neighborhood

Examples of org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood

54
55
56
57
58
59
60
61
62
63
64
      System.out.println(loadStats);
    }

    System.out.println("Run Users");
    UserSimilarity userSim = new EuclideanDistanceSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSim, model);
    recommender = new GenericUserBasedRecommender(model, neighborhood, userSim);
    for (int i = 0; i < LOOPS; i++){
      LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
      System.out.println(loadStats);
    }
View Full Code Here
69
70
71
72
73
74
75
76
77
78
79
  }

  @Test
  public void testFile() throws Exception {
    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(1, recommender.recommend(123, 3).size());
    assertEquals(0, recommender.recommend(234, 3).size());
    assertEquals(1, recommender.recommend(345, 3).size());
View Full Code Here
41
42
43
44
45
46
47
48
  private final Recommender recommender;

  public BookCrossingRecommender(DataModel dataModel, BookCrossingDataModel bcModel) throws TasteException {
    UserSimilarity similarity = new GeoUserSimilarity(bcModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(5, similarity, dataModel);
    recommender = new CachingRecommender(new GenericUserBasedRecommender(dataModel, neighborhood, similarity));
  }
View Full Code Here
60
61
62
63
64
65
66
67
68
69
70
    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());
View Full Code Here
80
81
82
83
84
85
86
87
88
89
90
    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);
View Full Code Here
128
129
130
131
132
133
134
135
136
137
138
    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());
  }
View Full Code Here
138
139
140
141
142
143
144
145
  }

  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);
  }
View Full Code Here
85
86
87
88
89
90
91
92
93
94
  }

  public void testUserLoad() throws Exception {
    DataModel model = createModel();
    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSimilarity, model);
    Recommender recommender =
            new CachingRecommender(new GenericUserBasedRecommender(model, neighborhood, userSimilarity));
    doTestLoad(recommender, 40);
  }
View Full Code Here
77
78
79
80
81
82
83
84
85
86
87
    model = new FileDataModel(testFile);
  }

  public void testFile() throws Exception {
    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(2, recommender.recommend("A123", 3).size());
    assertEquals(2, recommender.recommend("B234", 3).size());
    assertEquals(1, recommender.recommend("C345", 3).size());
View Full Code Here
69
70
71
72
73
74
75
76
77
78
79
  }

  @Test
  public void testFile() throws Exception {
    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(1, recommender.recommend(123, 3).size());
    assertEquals(0, recommender.recommend(234, 3).size());
    assertEquals(1, recommender.recommend(345, 3).size());
View Full Code Here
TOP

Related Classes of org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood

  • com.manning.hip.ch9.MovieUserEvaluator$MyRecommendBuilder
  • com.manning.hip.ch9.MovieUserRecommender
  • de.apaxo.bedcon.AnimalFoodRecommender
  • mia.recommender.ch02.EvaluatorIntro
  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro1
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
  • mia.recommender.ch04.GroupLens10MEvalIntro
  • mia.recommender.ch04.GroupLensDataModelIntro
  • mia.recommender.ch05.LibimsetiIREvalRunner
    • 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.