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

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

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    doTestLoad(recommender, 240);
  }

  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);
  }
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    }
    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());
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  @Override
  public long[] getUserNeighborhood(long userID) throws TasteException {
   
    DataModel dataModel = getDataModel();
    UserSimilarity userSimilarityImpl = getUserSimilarity();
   
    TopItems.Estimator<Long> estimator = new Estimator(userSimilarityImpl, userID, minSimilarity);
   
    LongPrimitiveIterator userIDs = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel.getUserIDs(),
      getSamplingRate());
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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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      new long[] {1, 2},
      new Double[][] {
              {null, 1.0, 2.0,  null, null, 6.0},
              {1.0, 8.0, null, 3.0,  4.0,  null},
      });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    similarity.setPreferenceInferrer(new PreferenceInferrer() {
      @Override
      public float inferPreference(long userID, long itemID) {
        return 1.0f;
      }
      @Override
      public void refresh(Collection<Refreshable> alreadyRefreshed) {
      }
    });

    assertEquals(-0.435285750066007, similarity.userSimilarity(1L, 2L), EPSILON);
  }
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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 += (float) 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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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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