public class HitRateEvaluator extends AbstractRecommenderEvaluator
Xia Ning and George Karypis, SLIM: Sparse Linear Methods for Top-N Recommender Systems, ICDM 2011.
They apply a leave-one-out validation method to evaluate the algorithm performance. In each run, each of the datasets is split into a training set and a testing set by randomly selecting one of the non-zero entries of each user and placing it into the testing set.
conf, similarities, similarityMatrix, topN
Constructor and Description |
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HitRateEvaluator() |
Modifier and Type | Method and Description |
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double |
evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
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evaluate, getConf, setTopN
public double evaluate(SparseMatrix testMatrix, RecommendedList recommendedList)
evaluate
in class AbstractRecommenderEvaluator
testMatrix
- the given test setrecommendedList
- the list of recommended itemsCopyright © 2017. All Rights Reserved.