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, 0.6},
                    {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, 0.4, 0.5, 0.9},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, 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 Exception {
    DataModel dataModel = getDataModel();
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
    return new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
  }
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public final class BookCrossingRecommender implements Recommender {

  private final Recommender recommender;

  public BookCrossingRecommender(DataModel bcModel) throws TasteException {
    UserSimilarity similarity = new PearsonCorrelationSimilarity(bcModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, 0.0, similarity, bcModel, 0.1);
    recommender = new CachingRecommender(new GenericUserBasedRecommender(bcModel, 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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    assertEquals(model.getItemIDsFromUser(456).size(), 4);
  }

  @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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    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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  }
 
  public void initRecommender() {
    try {
     
      PearsonCorrelationSimilarity pearsonSimilarity = new PearsonCorrelationSimilarity(model);
     
      // Java: Similarity between Wolf and Bear: 0.8196561646738477
      // R: corr(c(8,3,1),c(8,7,2)): 0.8196562
      System.out.println("Similarity between Wolf and Bear: "+pearsonSimilarity.userSimilarity(id2thing.toLongID("Wolf"), id2thing.toLongID("Bear")));
      // Similarity between Wolf and Rabbit: -0.6465846072812313
      // R: cor(c(8,3,1),c(2,1,10)): -0.6465846
      System.out.println("Similarity between Wolf and Rabbit: "+pearsonSimilarity.userSimilarity(id2thing.toLongID("Wolf"), id2thing.toLongID("Rabbit")));
      // Similarity between Wolf and Pinguin: -0.24019223070763077
      // R: cor(c(8,3,1),c(2,10,2)): -0.2401922
      System.out.println("Similarity between Wolf and Pinguin: "+pearsonSimilarity.userSimilarity(id2thing.toLongID("Wolf"), id2thing.toLongID("Pinguin")));
     
      GenericUserBasedRecommender recommender = new GenericUserBasedRecommender(model, new NearestNUserNeighborhood(3, pearsonSimilarity, model), pearsonSimilarity);
      for(RecommendedItem r : recommender.recommend(id2thing.toLongID("Wolf"), 3)) {
        // Pork:
        // (0.8196561646738477 * 8 + (-0.6465846072812313) * 1) / (0.8196561646738477 + (-0.6465846072812313)) = 34,15157 ~ 10
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  private static void recommend(String ratingsFile, int ... userIds)
      throws TasteException, IOException {
    DataModel model = new FileDataModel(new File(ratingsFile));

    UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

    UserNeighborhood neighborhood =
        new NearestNUserNeighborhood(
            100, similarity, model);
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