Mahout对于定制的GroupLens推荐进行评估
2015-05-12 08:21
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/* * 这段程序写的是测试定制的GroupLens的评估 * */ package byuser; import java.io.File; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.eval.RecommenderBuilder; import org.apache.mahout.cf.taste.eval.RecommenderEvaluator; import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator; import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood; import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender; import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity; import org.apache.mahout.cf.taste.model.DataModel; import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood; import org.apache.mahout.cf.taste.recommender.Recommender; import org.apache.mahout.cf.taste.similarity.UserSimilarity; import org.apache.mahout.cf.taste.similarity.precompute.example.GroupLensDataModel; public class GenericRecByGroupLens_Evalu { public GenericRecByGroupLens_Evalu() throws Exception{ DataModel model = new GroupLensDataModel(new File("E:\\mahout项目\\examples\\ratings.dat")); RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RecommenderBuilder recommenderBuilder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { UserSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05); System.out.println("GroupLens定制的推荐引擎的评测得分是: " + score); } public static void main(String[] args) throws Exception { // TODO Auto-generated method stub GenericRecByGroupLens_Evalu eva = new GenericRecByGroupLens_Evalu(); } }
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