### Question:
MahoutRecommender implements Recommender { public void evaluate(String taskName) { String itemmodelsPath = RecommendConfig.class.getResource("/").getPath() + "itemmodels.csv"; HadoopUtil.download(taskName, itemmodelsPath, false); RandomUtils.useTestSeed(); try { DataModel fileDataModel = new FileDataModel(new File(itemmodelsPath)); RecommenderEvaluator recommenderEvaluator = new RMSRecommenderEvaluator(); double evaluate = recommenderEvaluator.evaluate(new RecommenderBuilder() { @Override public org.apache.mahout.cf.taste.recommender.Recommender buildRecommender(final DataModel dataModel) throws TasteException { UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel); UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(2, userSimilarity, dataModel); return new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity); } }, new DataModelBuilder() { @Override public DataModel buildDataModel(final FastByIDMap<PreferenceArray> fastByIDMap) { for (Map.Entry<Long, PreferenceArray> entry : fastByIDMap.entrySet()) { System.out.println(entry.getKey() + " " + entry.getValue()); } return new GenericDataModel(fastByIDMap); } }, fileDataModel, 0.6, 1.0); System.out.println("评估结果:" + evaluate); } catch (TasteException | IOException e) { e.printStackTrace(); } }  @Override long[] recommend(String taskName, int userID); @Override long[] recommend(final String taskName, final Float threshold, final int userID); @Override long[] recommend(String taskName, String similarity, String neighborhood, Float threshold, int userID); void buildRecommend(String taskName); void evaluate(String taskName); void IRState(String taskName);  }### Answer:
@Test public void evaluate() { recommender.evaluate("mmsnsarticle"); }