數據集下載地址：http://grouplens.org/datasets/movielens/ 之前用的是100K的，現在需要下載MovieLens 10M,使用里面的ratings.dat

前提：因為文件不符合mahout要求的文件輸入格式，需要進行轉換，但是example里提供了一個解析這個文件的類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,如果沒有轉換數據集成為csv格式的DataModel dataModel = new GroupLensDataModel(new File("data/ratings.dat"));//皮爾遜相關系數，衡量用戶相似度UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel);//構建用戶鄰居，100個UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(100,userSimilarity, dataModel);//推薦引擎Recommender recommender = new GenericUserBasedRecommender(dataModel,userNeighborhood, userSimilarity);//運行LoadEvaluator.runLoad(recommender);} }

?運行試試，如果你的內存足夠大的話。

輸出結果：

我的文件還沒有下載下來呢！！！！！！！！！！

補上：

輸出結果：

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

?沒有輸出結果：

在代碼最后增加這么幾行代碼測試：

//增加推薦：//為用戶1推薦10件物品1,10List<RecommendedItem> recommendedItems = recommender.recommend(1, 10);//輸出for (RecommendedItem item : recommendedItems) {System.out.println(item);}

?查看輸出結果：還是沒有結果，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

?

轉載于:https://www.cnblogs.com/jsunday/p/3889947.html

總結

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