Examples of PearsonCorrelationSimilarity

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

An implementation of the Pearson correlation. For users X and Y, the following values are calculated:

sumX2: sum of the square of all X's preference values
sumY2: sum of the square of all Y's preference values
sumXY: sum of the product of X and Y's preference value for all items for which both X and Y express a preference

The correlation is then:

sumXY / sqrt(sumX2 * sumY2)

Note that this correlation "centers" its data, shifts the user's preference values so that each of their means is 0. This is necessary to achieve expected behavior on all data sets.

This correlation implementation is equivalent to the cosine measure correlation since the data it receives is assumed to be centered -- mean is 0. The correlation may be interpreted as the cosine of the angle between the two vectors defined by the users' preference values.

                    {0.4, 0.4, 0.5, 0.9},
                    {0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
                    {0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
            });


    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender2(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
    for (int i = 0; i < fewRecommended.size(); i++) {

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Examples of PearsonCorrelationSimilarity

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