下面是一个基本的JVAVA程序， RecommenderIntro.java

package xyz.pl8.recommenderintro;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.RecommendedItem;import org.apache.mahout.cf.taste.recommender.Recommender;import org.apache.mahout.cf.taste.similarity.UserSimilarity;import java.io.File;import java.util.List;public class RecommenderIntro {    public static void main(String[] args){        try{            // intro.csv格式 userId,itemId,rating            DataModel model = new FileDataModel(new File("/home/hadoop/intro.csv"));            System.out.println(model);            // 用户相似度            UserSimilarity similarity = new PearsonCorrelationSimilarity(model);            // K近邻用户            UserNeighborhood neighborhood = new NearestNUserNeighborhood(3,  similarity, model );            // 基于用户的推荐器            Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);            //  推荐物品            List
    
      recommendedItems = recommender.recommend(2, 2);            for (RecommendedItem item : recommendedItems){                System.out.println(item);            }        }catch (Exception e){            e.printStackTrace();        }    }}
    

下面是基于物品的多线程批推荐

package xyz.pl8.userrecommendermovielens;import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;import org.apache.mahout.cf.taste.impl.similarity.precompute.FileSimilarItemsWriter;import org.apache.mahout.cf.taste.impl.similarity.precompute.MultithreadedBatchItemSimilarities;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;import org.apache.mahout.cf.taste.similarity.ItemSimilarity;import org.apache.mahout.cf.taste.similarity.precompute.BatchItemSimilarities;import org.apache.mahout.cf.taste.similarity.precompute.SimilarItemsWriter;import java.io.File;import java.io.IOException;public class BatchItemSimilaritiesIntro {    public static void main(String[] args) throws IOException {        if (args.length !=1 ){            System.err.println("Need dataset file as argument!");            System.exit(-1);        }        File resultFile = new File(System.getProperty("java.io.tmpdir"), "similarity.csv");        DataModel dataModel = new MovieLensDataModel(new File(args[0]));        ItemSimilarity similarity = new LogLikelihoodSimilarity(dataModel);        ItemBasedRecommender recommender = new GenericItemBasedRecommender(dataModel, similarity);        BatchItemSimilarities batchItemSimilarities = new MultithreadedBatchItemSimilarities(recommender, 5);        SimilarItemsWriter writer = new FileSimilarItemsWriter(resultFile);        int numSimilarities = batchItemSimilarities.computeItemSimilarities(Runtime.getRuntime().availableProcessors(), 1,writer);        System.out.println("Computed " + numSimilarities + " for " +  " items and saved them to " + resultFile.getAbsolutePath());    }}

package xyz.pl8.userrecommendermovielens;import org.apache.commons.io.Charsets;import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;import org.apache.mahout.common.iterator.FileLineIterable;import org.omg.CORBA.PUBLIC_MEMBER;import java.io.*;import java.nio.charset.Charset;import java.util.regex.Pattern;public class MovieLensDataModel extends FileDataModel {    private static String COLON_DELIMITER = "::";    private  static Pattern COLON_DELIMITTER_PATTERN = Pattern.compile(COLON_DELIMITER);    public MovieLensDataModel(File ratingsFile) throws IOException{        super(convertFile(ratingsFile));    }    public static File convertFile(File originalFile) throws IOException{        File resultFile = new File(System.getProperty("java.io.tmpdir"), "ratings.csv");        if (resultFile.exists()){            resultFile.delete();        }        try {            Writer writer = new OutputStreamWriter(new FileOutputStream(resultFile), Charsets.UTF_8);            for (String line : new FileLineIterable(originalFile, false)) {                int lastIndex = line.lastIndexOf(COLON_DELIMITER);                if (lastIndex < 0) {                    throw new IOException("Invalid data!");                }                String subLine = line.substring(0, lastIndex);                String convertedSubLne = COLON_DELIMITTER_PATTERN.matcher(subLine).replaceAll(",");                lastIndex = convertedSubLne.lastIndexOf(",");                if (lastIndex <= 0) {                    continue;                }                writer.write(convertedSubLne);                writer.write('\n');            }            writer.close();        }catch (Exception e){            resultFile.delete();        }        return  resultFile;    }}