Examples of org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity

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

Examples of org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity

org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity

A "generic" {@link ItemSimilarity} which takes a static list of precomputed item similarities and bases itsresponses on that alone. The values may have been precomputed offline by another process, stored in a file, and then read and fed into an instance of this class.

This is perhaps the best {@link ItemSimilarity} to use with{@link org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender}, for now, since the point of item-based recommenders is that they can take advantage of the fact that item similarity is relatively static, can be precomputed, and then used in computation to gain a significant performance advantage.

      for (int j = i + 1; j < 6; j++) {
        similarities.add(
            new GenericItemSimilarity.ItemItemSimilarity(i, j, 1.0 / (1.0 + i + 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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    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, 4, null, true);
    List<RecommendedItem> rescoredRecommended = recommender.recommend(1, 4, new ReversingRescorer<Long>(), true);
    assertNotNull(originalRecommended);
    assertNotNull(rescoredRecommended);

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    DataModel dataModel = getDataModel();
    Collection<GenericItemSimilarity.ItemItemSimilarity> similarities = Lists.newArrayList();
    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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Related Classes of org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity

org.apache.mahout.cf.taste.impl.common.FastIDSet

org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommenderTest

org.apache.mahout.cf.taste.impl.similarity.file.FileItemSimilarity

org.apache.mahout.cf.taste.impl.similarity.file.FileItemSimilarityTest

org.apache.mahout.cf.taste.impl.similarity.jdbc.AbstractJDBCInMemoryItemSimilarity

org.apache.mahout.cf.taste.model.Item

java.util.NoSuchElementException

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