标签：blog java os io 文件 for cti ar
通过估计偏好值来生成推荐结果并非绝对必要。给出一个从优到劣的推荐列表对于许多场景都够用了，而不必包含估计的偏好值。
查准率：在top结果中相关结果的比例
查全率：所有相关结果，包含在top结果中的比例
对上个例子进行测试：
package mahout;
import java.io.File;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.IRStatistics;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
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.common.RandomUtils;
public class IRStatsEvalutator {
public static void main(String[] args) throws Exception {
RandomUtils.useTestSeed();
DataModel dataModel = new FileDataModel(new File("data/intro.csv"));
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
//用于生成推荐引擎的构建器，与上一例子实现相同
RecommenderBuilder builder = new RecommenderBuilder() {
public Recommender buildRecommender(DataModel model) throws TasteException {
// TODO Auto-generated method stub
//用户相似度，多种方法
UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
//用户邻居
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
//一个推荐器
return new GenericUserBasedRecommender(model, neighborhood, similarity);
}
};
//评估推荐2个结果时的查准率和查全率
IRStatistics statistics = evaluator.evaluate(builder, null, dataModel, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
System.out.println("查准率："+statistics.getPrecision());
System.out.println("查全率："+statistics.getRecall());
}
}
输出结果：
14/08/04 09:46:21 INFO file.FileDataModel: Creating FileDataModel for file data\intro.csv 14/08/04 09:46:21 INFO file.FileDataModel: Reading file info... 14/08/04 09:46:21 INFO file.FileDataModel: Read lines: 21 14/08/04 09:46:21 INFO model.GenericDataModel: Processed 5 users 14/08/04 09:46:21 INFO model.GenericDataModel: Processed 5 users 14/08/04 09:46:21 INFO model.GenericDataModel: Processed 5 users 14/08/04 09:46:21 INFO eval.GenericRecommenderIRStatsEvaluator: Evaluated with user 2 in 31ms 14/08/04 09:46:21 INFO eval.GenericRecommenderIRStatsEvaluator: Precision/recall/fall-out/nDCG/reach: 1.0 / 1.0 / 0.0 / 1.0 / 1.0 14/08/04 09:46:21 INFO model.GenericDataModel: Processed 5 users 14/08/04 09:46:21 INFO eval.GenericRecommenderIRStatsEvaluator: Evaluated with user 4 in 0ms 14/08/04 09:46:21 INFO eval.GenericRecommenderIRStatsEvaluator: Precision/recall/fall-out/nDCG/reach: 0.75 / 1.0 / 0.08333333333333333 / 1.0 / 1.0 查准率：0.75 查全率：1.0
文件描述：见《mahout in Action》
mahout推荐3-评估查准率和查全率,布布扣,bubuko.com
标签：blog java os io 文件 for cti ar
原文地址：http://www.cnblogs.com/jsunday/p/3889437.html
