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

Examples of org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity

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    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.1));
    users.add(getUser("test2", 0.2, 0.6));
    users.add(getUser("test3", 0.4, 0.9));
    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(0, recommended.size());
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    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);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
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    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);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> originalRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> rescoredRecommended =
            recommender.recommend("test1", 2, new ReversingRescorer<Item>());
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    users.add(getUser("test1", 0.1, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    users.add(getUser("test4", 0.7, 0.3, 0.8, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    assertEquals(0.9, recommender.estimatePreference("test3", "3"));
  }
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    users.add(getUser("test1", 0.1, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    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());
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    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);
    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++) {
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    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);
    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>());
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    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"));
    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());
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    assertEquals(0, mostSimilar.size());
  }

  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);
  }
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    doTestLoad(recommender, 60);
  }

  public void testItemLoad() throws Exception {
    DataModel model = createModel();
    ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(model);
    Recommender recommender = new CachingRecommender(new GenericItemBasedRecommender(model, itemSimilarity));
    doTestLoad(recommender, 240);
  }
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