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

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

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                    {0.2, 0.3, 0.3},
                    {0.4, 0.3, 0.5},
                    {0.7, 0.3, 0.8, 0.9},
            });

    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    assertEquals(0.9f, recommender.estimatePreference(3, 3), EPSILON);
  }
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                    {0.4, 0.3, 0.5},
                    {0.7, 0.3, 0.8},
            });


    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
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                    {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},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
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            new Double[][] {
                    {0.1, 0.2},
                    {0.2, 0.3, 0.3, 0.6},
                    {0.4, 0.5, 0.5, 0.9},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    List<RecommendedItem> originalRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> rescoredRecommended =
        recommender.recommend(1, 2, new ReversingRescorer<Long>());
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                    {0.1, 0.2},
                    {0.2, 0.3, 0.3, 0.6},
                    {0.4, 0.4, 0.5, 0.9},
                    {null, null, null, null, 1.0},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel);
    UserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    long[] mostSimilar = recommender.mostSimilarUserIDs(4, 3);
    assertNotNull(mostSimilar);
    assertEquals(0, mostSimilar.length);
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    assertEquals(0, mostSimilar.length);
  }

  private static UserBasedRecommender buildRecommender() throws TasteException {
    DataModel dataModel = getDataModel();
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    return new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
  }
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    model = new FileDataModel(testFile);
  }

  @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());
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                    {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},
            });
    ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    Recommender recommender = new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
    List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
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            new Double[][] {
                    {0.1, 0.2},
                    {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>());
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    assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
  }

  private static Recommender buildRecommender() throws TasteException {
    DataModel dataModel = getDataModel();
    ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    return new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
  }
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