Examples of PreferenceInferrer

org.apache.mahout.cf.taste.similarity.PreferenceInferrer

Implementations of this interface compute an inferred preference for a {@link User} and an {@link Item}that the user has not expressed any preference for. This might be an average of other preferences scores from that user, for example. This technique is sometimes called "default voting".

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

/** <p>Tests {@link AveragingPreferenceInferrer}.</p> */
public final class AveragingPreferenceInferrerTest extends TasteTestCase {


  public void testInferrer() throws TasteException {
    DataModel model = getDataModel(new long[] {1}, new Double[][] {{3.0,-2.0,5.0}});
    PreferenceInferrer inferrer = new AveragingPreferenceInferrer(model);
    double inferred = inferrer.inferPreference(1, 3);
    assertEquals(2.0, inferred);
  }

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


  public void testInferrer() throws TasteException {
    User user1 = getUser("test1", 3.0, -2.0, 5.0);
    Item item = new GenericItem<String>("3");
    DataModel model = new GenericDataModel(Collections.singletonList(user1));
    PreferenceInferrer inferrer = new AveragingPreferenceInferrer(model);
    double inferred = inferrer.inferPreference(user1, item);
    assertEquals(2.0, inferred);
  }

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

      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

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

public final class AveragingPreferenceInferrerTest extends TasteTestCase {


  @Test
  public void testInferrer() throws TasteException {
    DataModel model = getDataModel(new long[] {1}, new Double[][] {{3.0,-2.0,5.0}});
    PreferenceInferrer inferrer = new AveragingPreferenceInferrer(model);
    double inferred = inferrer.inferPreference(1, 3);
    assertEquals(2.0, inferred, EPSILON);
  }

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