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

Examples of org.apache.mahout.cf.taste.recommender.Recommender

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                    {0.7, 0.3, 0.8, 0.9},
            });

    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    assertEquals(0.9f, recommender.estimatePreference(3, 3));
  }
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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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            });


    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    // item one should be recommended because it has a greater rating/score
    assertEquals(2, firstRecommended.getItemID());
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    assertEquals(originalRecommended.get(0).getItemID(), rescoredRecommended.get(1).getItemID());
    assertEquals(originalRecommended.get(1).getItemID(), rescoredRecommended.get(0).getItemID());
  }

  public void testEstimatePref() throws Exception {
    Recommender recommender = buildRecommender();
    assertEquals(0.34803885284992736, recommender.estimatePreference(1, 2), EPSILON);
  }
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                    {0.0, 0.3},
                    {0.2, 0.3, 0.3},
                    {0.4, 0.3, 0.5},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    // item one should be recommended because it has a greater rating/score
    assertEquals(2, firstRecommended.getItemID());
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                    {0.1, 0.2},
                    {0.2, 0.3, 0.6},
                    {0.3, 0.3, 0.3},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    assertEquals(0.3257f, recommender.estimatePreference(1, 2), EPSILON);
  }
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import java.util.List;

public final class RandomRecommenderTest extends TasteTestCase {

  public void testRecommender() throws Exception {
    Recommender recommender = new RandomRecommender(getDataModel());
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(2, firstRecommended.getItemID());
    recommender.refresh(null);
    assertEquals(2, firstRecommended.getItemID());
  }
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/** <p>Tests {@link GenericItemBasedRecommender}.</p> */
public final class GenericItemBasedRecommenderTest extends TasteTestCase {

  public void testRecommender() throws Exception {
    Recommender recommender = buildRecommender();
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.18, firstRecommended.getValue(), EPSILON);
    recommender.refresh(null);
    recommended = recommender.recommend(1, 1);
    firstRecommended = recommended.get(0);   
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.18, firstRecommended.getValue(), EPSILON);
  }
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        similarities.add(
            new GenericItemSimilarity.ItemItemSimilarity(i, j, 1.0 / (1.0 + (double) i + (double) j)));
      }
    }
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    Recommender recommender = new GenericItemBasedRecommender(dataModel, 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());
    }
    recommender.refresh(null);
    for (int i = 0; i < fewRecommended.size(); i++) {
      assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
    }
  }
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    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 3, 0.2));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.7));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.5));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, 0.9));
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    Recommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
    List<RecommendedItem> originalRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> rescoredRecommended =
        recommender.recommend(1, 2, new ReversingRescorer<Long>());
    assertNotNull(originalRecommended);
    assertNotNull(rescoredRecommended);
    assertEquals(2, originalRecommended.size());
    assertEquals(2, rescoredRecommended.size());
    assertEquals(originalRecommended.get(0).getItemID(), rescoredRecommended.get(1).getItemID());
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