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

Examples of org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender$Estimator

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      throws TasteException, IOException {
    UserSimilarity similarity = new EuclideanDistanceSimilarity(model);
    UserNeighborhood neighborhood =
        new NearestNUserNeighborhood(2, similarity, model);
    delegate =
        new GenericUserBasedRecommender(model, neighborhood, similarity);
    this.model = model;
    FastIDSet[] menWomen = GenderRescorer.parseMenWomen(readResourceToTempFile("gender.dat"));
    men = menWomen[0];
    women = menWomen[1];
    usersRateMoreMen = new FastIDSet(50000);
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      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(100, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05);
    System.out.println(score);
  }
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    DataModel model = new GroupLensDataModel(new File("ratings.dat"));
    UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood =
      new NearestNUserNeighborhood(100, similarity, model);
    Recommender recommender =
      new GenericUserBasedRecommender(model, neighborhood, similarity);
    LoadEvaluator.runLoad(recommender);
  }
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      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    // Use 70% of the data to train; test using the other 30%.
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.7, 1.0);
    System.out.println(score);
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      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    DataModelBuilder modelBuilder = new DataModelBuilder() {
      @Override
      public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
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      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    // Evaluate precision and recall "at 2":
    IRStatistics stats = evaluator.evaluate(recommenderBuilder,
                                            null, model, null, 2,
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  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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  }

  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(123, 3).size());
    assertEquals(2, recommender.recommend(234, 3).size());
    assertEquals(1, recommender.recommend(345, 3).size());

    // Make sure this doesn't throw an exception
    model.refresh(null);
  }
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  private final Recommender recommender;

  public BookCrossingRecommender(DataModel bcModel) throws TasteException {
    UserSimilarity similarity = new CachingUserSimilarity(new EuclideanDistanceSimilarity(bcModel), bcModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, 0.2, similarity, bcModel, 0.2);
    recommender = new GenericUserBasedRecommender(bcModel, neighborhood, similarity);
  }
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    }

    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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