mahout推荐10-尝试GroupLens数据集
数据集下载地址：http://grouplens.org/datasets/movielens/ 之前用的是100K的，现在需要下载MovieLens 10M,使用里面的ratings.dat
前提：因为文件不符合mahout要求的文件输入格式，需要进行转换，但是example里提供了一个解析这个文件的类GrouplensDataModel，所以直接用了。
package mahout;
import java.io.File;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.eval.LoadEvaluator;
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 GroupLensDataModelTest {
public static void main(String[] args) throws Exception {
//使用定制的GrouplensDataModel,如果没有转换数据集成为csv格式的
DataModel dataModel = new GroupLensDataModel(new File(
"data/ratings.dat"));
//皮尔逊相关系数，衡量用户相似度
UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(
dataModel);
//构建用户邻居，100个
UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(100,
userSimilarity, dataModel);
//推荐引擎
Recommender recommender = new GenericUserBasedRecommender(dataModel,
userNeighborhood, userSimilarity);
//运行
LoadEvaluator.runLoad(recommender);
}
}
运行试试，如果你的内存足够大的话。
输出结果：
我的文件还没有下载下来呢！！！！！！！！！！
补上：
输出结果：
14/08/05 10:05:13 INFO file.FileDataModel: Creating FileDataModel for file C:\Users\ADMINI~1\AppData\Local\Temp\ratings.txt 14/08/05 10:05:17 INFO file.FileDataModel: Reading file info... 14/08/05 10:05:18 INFO file.FileDataModel: Processed 1000000 lines 14/08/05 10:05:19 INFO file.FileDataModel: Processed 2000000 lines 14/08/05 10:05:20 INFO file.FileDataModel: Processed 3000000 lines 14/08/05 10:05:21 INFO file.FileDataModel: Processed 4000000 lines 14/08/05 10:05:23 INFO file.FileDataModel: Processed 5000000 lines 14/08/05 10:05:24 INFO file.FileDataModel: Processed 6000000 lines 14/08/05 10:05:25 INFO file.FileDataModel: Processed 7000000 lines 14/08/05 10:05:26 INFO file.FileDataModel: Processed 8000000 lines 14/08/05 10:05:27 INFO file.FileDataModel: Processed 9000000 lines 14/08/05 10:05:30 INFO file.FileDataModel: Processed 10000000 lines 14/08/05 10:05:30 INFO file.FileDataModel: Read lines: 10000054 14/08/05 10:05:31 INFO model.GenericDataModel: Processed 10000 users 14/08/05 10:05:31 INFO model.GenericDataModel: Processed 20000 users 14/08/05 10:05:33 INFO model.GenericDataModel: Processed 30000 users 14/08/05 10:05:33 INFO model.GenericDataModel: Processed 40000 users 14/08/05 10:05:34 INFO model.GenericDataModel: Processed 50000 users 14/08/05 10:05:34 INFO model.GenericDataModel: Processed 60000 users 14/08/05 10:05:35 INFO model.GenericDataModel: Processed 69878 users 14/08/05 10:05:39 INFO eval.AbstractDifferenceRecommenderEvaluator: Starting timing of 982 tasks in 4 threads 14/08/05 10:05:39 INFO eval.StatsCallable: Average time per recommendation: 163ms 14/08/05 10:05:39 INFO eval.StatsCallable: Approximate memory used: 445MB / 815MB 14/08/05 10:05:39 INFO eval.StatsCallable: Unable to recommend in 0 cases
没有输出结果：
在代码最后增加这么几行代码测试：
//增加推荐：
//为用户1推荐10件物品1,10
List<RecommendedItem> recommendedItems = recommender.recommend(1, 10);
//输出
for (RecommendedItem item : recommendedItems) {
System.out.println(item);
}
查看输出结果：还是没有结果，怪了，后期再搞搞。
14/08/05 10:09:48 INFO file.FileDataModel: Creating FileDataModel for file C:\Users\ADMINI~1\AppData\Local\Temp\ratings.txt 14/08/05 10:09:48 INFO file.FileDataModel: Reading file info... 14/08/05 10:09:49 INFO file.FileDataModel: Processed 1000000 lines 14/08/05 10:09:50 INFO file.FileDataModel: Processed 2000000 lines 14/08/05 10:09:52 INFO file.FileDataModel: Processed 3000000 lines 14/08/05 10:09:52 INFO file.FileDataModel: Processed 4000000 lines 14/08/05 10:09:54 INFO file.FileDataModel: Processed 5000000 lines 14/08/05 10:09:56 INFO file.FileDataModel: Processed 6000000 lines 14/08/05 10:09:56 INFO file.FileDataModel: Processed 7000000 lines 14/08/05 10:09:57 INFO file.FileDataModel: Processed 8000000 lines 14/08/05 10:09:58 INFO file.FileDataModel: Processed 9000000 lines 14/08/05 10:10:00 INFO file.FileDataModel: Processed 10000000 lines 14/08/05 10:10:00 INFO file.FileDataModel: Read lines: 10000054 14/08/05 10:10:01 INFO model.GenericDataModel: Processed 10000 users 14/08/05 10:10:01 INFO model.GenericDataModel: Processed 20000 users 14/08/05 10:10:02 INFO model.GenericDataModel: Processed 30000 users 14/08/05 10:10:02 INFO model.GenericDataModel: Processed 40000 users 14/08/05 10:10:02 INFO model.GenericDataModel: Processed 50000 users 14/08/05 10:10:03 INFO model.GenericDataModel: Processed 60000 users 14/08/05 10:10:06 INFO model.GenericDataModel: Processed 69878 users 14/08/05 10:10:08 INFO eval.AbstractDifferenceRecommenderEvaluator: Starting timing of 985 tasks in 4 threads 14/08/05 10:10:08 INFO eval.StatsCallable: Average time per recommendation: 116ms 14/08/05 10:10:08 INFO eval.StatsCallable: Approximate memory used: 578MB / 795MB 14/08/05 10:10:08 INFO eval.StatsCallable: Unable to recommend in 0 cases
