Package org.apache.mahout.cf.taste.similarity

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

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import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

class KnnBasedRecommender {

  Recommender buildRecommender(DataModel model) {
    ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    return new KnnItemBasedRecommender(model, similarity, optimizer, 10);
  }
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    doTestLoad(recommender, 60);
  }

  public void testItemLoad() throws Exception {
    DataModel model = createModel();
    ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(model);
    Recommender recommender = new CachingRecommender(new GenericItemBasedRecommender(model, itemSimilarity));
    doTestLoad(recommender, 240);
  }
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            new Double[][] {
                    {1.0, 2.0},
                    {2.0, 5.0},
                    {3.0, 6.0},
            });
    ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
    double correlation = itemSimilarity.itemSimilarity(0, 1);
    assertCorrelationEquals(0.9901922307076306, correlation);
  }
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            new Double[][] {
                    {1.0, 2.0},
                    {2.0, 5.0},
                    {3.0, 6.0},
            });
    ItemSimilarity itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
    double correlation = itemSimilarity.itemSimilarity(0, 1);
    assertCorrelationEquals(0.8974062142054332, correlation);
  }
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            new Double[][] {
                    {1.0, 2.0},
                    {2.0, 5.0},
                    {3.0, 6.0},
            });
    ItemSimilarity otherSimilarity = new PearsonCorrelationSimilarity(dataModel);
    ItemSimilarity itemSimilarity = new GenericItemSimilarity(otherSimilarity, dataModel);
    assertCorrelationEquals(1.0, itemSimilarity.itemSimilarity(0, 0));
    assertCorrelationEquals(0.960768922830523, itemSimilarity.itemSimilarity(0, 1));
  }
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      for (int j = i + 1; j < 6; j++) {
        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());
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    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.5));
    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);
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    Collection<GenericItemSimilarity.ItemItemSimilarity> similarities =
        new ArrayList<GenericItemSimilarity.ItemItemSimilarity>(3);
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.5));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.0));
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    return new GenericItemBasedRecommender(dataModel, similarity);
  }
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    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 1, 1.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, -0.1));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(2, 4, 0.1));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(3, 4, -0.5));
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    return new GenericItemBasedRecommender(dataModel, similarity);
  }
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            new Double[][] {
                    {1.0, 2.0},
                    {2.0, 5.0},
                    {3.0, 6.0},
            });
    ItemSimilarity itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
    double correlation = itemSimilarity.itemSimilarity(0, 1);
    assertCorrelationEquals(0.889954122647528, correlation);
  }
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Related Classes of org.apache.mahout.cf.taste.similarity.ItemSimilarity

  • io.prediction.examples.java.recommendations.tutorial5.MahoutAlgoModel
  • mia.recommender.ch04.KnnBasedRecommender
  • org.apache.mahout.cf.taste.example.kddcup.track1.Track1Recommender
  • org.apache.mahout.cf.taste.example.kddcup.track2.Track2Recommender
  • org.apache.mahout.cf.taste.impl.eval.LoadEvaluationRunner
  • org.apache.mahout.cf.taste.impl.LoadTest
  • org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommenderTest
  • org.apache.mahout.cf.taste.impl.recommender.knn.KnnItemBasedRecommenderTest
  • org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarityTest
  • org.apache.mahout.cf.taste.impl.similarity.file.FileItemSimilarityTest
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