Package org.apache.mahout.cf.taste.neighborhood

Examples of org.apache.mahout.cf.taste.neighborhood.UserNeighborhood

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  }

  public LibimsetiRecommender(DataModel model)
      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"));
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        new GenericRecommenderIRStatsEvaluator();
      RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {
          UserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
          UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
          return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
        }
      };
      IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 10, Double.NaN, 0.1);
      System.out.println(stats);
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      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @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);
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  }

  public static void main(String[] args) throws Exception {
    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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    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @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%.
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      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @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() {
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    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @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":
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      new GenericRecommenderIRStatsEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new LogLikelihoodSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
        return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    DataModelBuilder modelBuilder = new DataModelBuilder() {
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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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                    {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++) {
      assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
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Related Classes of org.apache.mahout.cf.taste.neighborhood.UserNeighborhood

  • com.manning.hip.ch9.MovieUserEvaluator$MyRecommendBuilder
  • com.manning.hip.ch9.MovieUserRecommender
  • mia.recommender.ch02.EvaluatorIntro
  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro1
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
  • mia.recommender.ch04.GroupLens10MEvalIntro
  • mia.recommender.ch04.GroupLensDataModelIntro
  • mia.recommender.ch05.LibimsetiIREvalRunner
  • mia.recommender.ch05.LibimsetiRecommender
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