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

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

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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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  public void testSimpleItemWeighted() throws Exception {
    User user1 = getUser("test1", 1.0, 2.0);
    User user2 = getUser("test2", 2.0, 5.0);
    User user3 = getUser("test3", 3.0, 6.0);
    DataModel dataModel = getDataModel(user1, user2, user3);
    ItemSimilarity itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
    double correlation = itemSimilarity.itemSimilarity(dataModel.getItem("0"), dataModel.getItem("1"));
    assertCorrelationEquals(0.8974062142054332, correlation);
  }
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  public void testSimpleItemWeighted() throws Exception {
    User user1 = getUser("test1", 1.0, 2.0);
    User user2 = getUser("test2", 2.0, 5.0);
    User user3 = getUser("test3", 3.0, 6.0);
    DataModel dataModel = getDataModel(user1, user2, user3);
    ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
    double correlation = itemSimilarity.itemSimilarity(dataModel.getItem("0"), dataModel.getItem("1"));
    assertCorrelationEquals(0.9901922307076306, correlation);
  }
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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 GenericItemSimilarity.ItemItemSimilarity(new GenericItem<String>(String.valueOf(i)),
                                                               new GenericItem<String>(String.valueOf(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("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
      assertEquals(fewRecommended.get(i).getItem(), moreRecommended.get(i).getItem());
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    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item3, 0.5));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item4, 0.2));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item3, 0.7));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item4, 0.5));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item3, item4, 0.9));
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    Recommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
    List<RecommendedItem> originalRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> rescoredRecommended =
            recommender.recommend("test1", 2, new ReversingRescorer<Item>());
    assertNotNull(originalRecommended);
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    Item item1 = new GenericItem<String>("0");
    Item item2 = new GenericItem<String>("1");
    Item item3 = new GenericItem<String>("2");
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item2, 1.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item1, item3, 0.5));
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    return new GenericItemBasedRecommender(dataModel, similarity);
  }
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    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item4, 0.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item2, item2, 1.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item3, item4, -0.1));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item3, item5, 0.1));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(item4, item5, -0.5));
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    return new GenericItemBasedRecommender(dataModel, similarity);
  }
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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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