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

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

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  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder, model, null, 5, 0.2, 1.0);
    assertNotNull(stats);
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  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
    double eval = evaluator.evaluate(builder, model, 0.85, 1.0);
    assertEquals(0.3004147161079469, eval, EPSILON);
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public final class JesterRecommender implements Recommender {
 
  private final Recommender recommender;
 
  public JesterRecommender(DataModel dataModel) throws TasteException {
    recommender = new CachingRecommender(new SlopeOneRecommender(dataModel));
  }
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  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
    double eval = evaluator.evaluate(builder, null, model, 0.85, 1.0);
    assertEquals(0.3481984752619784, eval, EPSILON);
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  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderEvaluator evaluator =
        new AverageAbsoluteDifferenceRecommenderEvaluator();
    double eval = evaluator.evaluate(builder, null, model, 0.85, 1.0);
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  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder, null, model, null, 1, 0.2, 1.0);
    assertNotNull(stats);
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   *
   * @param dataModel data model
   * @throws TasteException if an error occurs while initializing this
   */
  public GroupLensRecommender(DataModel dataModel) throws TasteException {
    recommender = new CachingRecommender(new SlopeOneRecommender(dataModel));
  }
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public final class NetflixRecommender implements Recommender {

  private final Recommender recommender;

  public NetflixRecommender(DataModel dataModel) throws TasteException {
    recommender = new SlopeOneRecommender(dataModel);
  }
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   *
   * @param dataModel data model
   * @throws TasteException if an error occurs while initializing this {@link GroupLensRecommender}
   */
  public GroupLensRecommender(DataModel dataModel) throws TasteException {
    recommender = new CachingRecommender(new SlopeOneRecommender(dataModel));
  }
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public final class JesterRecommender implements Recommender {

  private final Recommender recommender;

  public JesterRecommender(DataModel dataModel) throws TasteException {
    recommender = new CachingRecommender(new SlopeOneRecommender(dataModel));
  }
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Related Classes of org.apache.mahout.cf.taste.impl.recommender.slopeone.SlopeOneRecommender$Estimator

  • mia.recommender.ch04.SlopeOneJDBC
  • mia.recommender.ch04.SlopeOneNoWeighting
  • mia.recommender.ch05.LimitedMemoryDiffStorage
  • org.apache.mahout.cf.taste.example.grouplens.GroupLensRecommender
  • org.apache.mahout.cf.taste.example.jester.JesterRecommender
  • org.apache.mahout.cf.taste.example.netflix.NetflixRecommender
  • org.apache.mahout.cf.taste.impl.common.FastIDSet
  • org.apache.mahout.cf.taste.impl.common.FullRunningAverage
  • org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator
  • org.apache.mahout.cf.taste.impl.common.RunningAverage
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