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

Examples of org.apache.mahout.cf.taste.similarity.UserSimilarity

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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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  protected float doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID) throws TasteException {
    if (theNeighborhood.length == 0) {
      return Float.NaN;
    }
    DataModel dataModel = getDataModel();
    UserSimilarity similarity = getSimilarity();
    float totalSimilarity = 0.0f;
    boolean foundAPref = false;
    for (long userID : theNeighborhood) {
      // See GenericItemBasedRecommender.doEstimatePreference() too
      if ((userID != theUserID) && (dataModel.getPreferenceValue(userID, itemID) != null)) {
        foundAPref = true;
        totalSimilarity += similarity.userSimilarity(theUserID, userID);
      }
    }
    return foundAPref ? totalSimilarity : Float.NaN;
  }
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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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      LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
      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);
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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 final class BookCrossingRecommender implements Recommender {

  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));
  }
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  public Collection<User> getUserNeighborhood(Object userID) throws TasteException {
    log.trace("Computing neighborhood around user ID '{}'", userID);

    DataModel dataModel = getDataModel();
    User theUser = dataModel.getUser(userID);
    UserSimilarity userSimilarityImpl = getUserSimilarity();

    TopItems.Estimator<User> estimator = new Estimator(userSimilarityImpl, theUser, minSimilarity);

    List<User> neighborhood = TopItems.getTopUsers(n, dataModel.getUsers(), null, estimator);
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Related Classes of org.apache.mahout.cf.taste.similarity.UserSimilarity

  • com.manning.hip.ch9.MovieUserEvaluator$MyRecommendBuilder
  • com.manning.hip.ch9.MovieUserRecommender
  • mia.recommender.ch02.EvaluatorIntro
  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch02.UserBaseRecommenderEvaluation
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro1
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
  • mia.recommender.ch04.ClusterBasedRecommender
  • mia.recommender.ch04.GroupLens10MEvalIntro
  • mia.recommender.ch04.GroupLensDataModelIntro
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