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mahout推荐4-评估GroupLens数据集

时间:2014-08-04 10:55:16      阅读:764      评论:0      收藏:0      [点我收藏+]

标签:blog   java   使用   io   文件   数据   for   art   

使用GroupLens数据集ua.base

这是一个tab分割的文件,用户Id,物品Id,评分(偏好值),以及附加信息。可用吗?之前使用的是CSV格式,现在是tsv格式,可用,使用FileDataModel

对mahout推荐2中的评估程序使用这个数据集测试:

package mahout;

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.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 TestRecommenderEvaluator {

	public static void main(String[] args) throws Exception {
		//强制每次生成相同的随机值,生成可重复的结果
		RandomUtils.useTestSeed();
		//数据装填
		//DataModel model = new FileDataModel(new File("data/intro.csv"));
		DataModel model = new FileDataModel(new File("data/ua.base"));
		//推荐评估,使用平均值
		RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
		//推荐评估,使用均方差
		//RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
		//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);
			}
		};
		//推荐程序评估值(平均差值)训练90%的数据,测试数据10%,《mahout in Action》使用的是0.7,但是出现结果为NaN
		double score = evaluator.evaluate(builder, null, model, 0.9, 1.0);
		System.out.println(score);
	}
}

 结果输出:

14/08/04 09:52:38 INFO file.FileDataModel: Creating FileDataModel for file data\ua.base
14/08/04 09:52:38 INFO file.FileDataModel: Reading file info...
14/08/04 09:52:38 INFO file.FileDataModel: Read lines: 90570
14/08/04 09:52:38 INFO model.GenericDataModel: Processed 943 users
14/08/04 09:52:38 INFO eval.AbstractDifferenceRecommenderEvaluator: Beginning evaluation using 0.9 of FileDataModel[dataFile:D:\workspace\zoodemo\data\ua.base]
14/08/04 09:52:38 INFO model.GenericDataModel: Processed 943 users
14/08/04 09:52:38 INFO eval.AbstractDifferenceRecommenderEvaluator: Beginning evaluation of 878 users
14/08/04 09:52:38 INFO eval.AbstractDifferenceRecommenderEvaluator: Starting timing of 878 tasks in 4 threads
14/08/04 09:52:39 INFO eval.StatsCallable: Average time per recommendation: 39ms
14/08/04 09:52:39 INFO eval.StatsCallable: Approximate memory used: 16MB / 79MB
14/08/04 09:52:39 INFO eval.StatsCallable: Unable to recommend in 114 cases
14/08/04 09:52:43 INFO eval.AbstractDifferenceRecommenderEvaluator: Evaluation result: 0.9375000000000002
0.9375000000000002

 现在是基于100 000 个偏好值,而不是少数几个

结果大约为0.9 在1到5的区间内,这个值偏离了将近一个点,不算太好。

也许我们正在使用的这个特定Recommender实现并不是最优的。

 

mahout推荐4-评估GroupLens数据集,布布扣,bubuko.com

mahout推荐4-评估GroupLens数据集

标签:blog   java   使用   io   文件   数据   for   art   

原文地址:http://www.cnblogs.com/jsunday/p/3889465.html

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