向MapReduce转换：形成推荐

<strong><span style="font-size:18px;">/*** * @author YangXin * @info 向MapReduce转换：形成推荐 ，处理来自向量的推荐结果。 */package unitSix;import java.io.IOException;import java.util.ArrayList;import java.util.Collections;import java.util.Iterator;import java.util.List;import java.util.PriorityQueue;import java.util.Queue;import org.apache.hadoop.mapreduce.Reducer;import org.apache.mahout.cf.taste.hadoop.RecommendedItemsWritable;import org.apache.mahout.cf.taste.impl.recommender.ByValueRecommendedItemComparator;import org.apache.mahout.cf.taste.impl.recommender.GenericRecommendedItem;import org.apache.mahout.cf.taste.recommender.RecommendedItem;import org.apache.mahout.math.VarLongWritable;import org.apache.mahout.math.Vector;import org.apache.mahout.math.VectorWritable;import org.apache.mahout.math.map.OpenIntLongHashMap;public class AggregateAndRecommendReducer extends Reducer<VarLongWritable, VectorWritable, VarLongWritable, RecommendedItemsWritable>{public void reduce(VarLongWritable key, Iterable<VectorWritable> values, Context context) throws IOException, InterruptedException{int recommendationsPerUser = 10;    OpenIntLongHashMap indexItemIDMap = null;Vector recommendationVector = null;for(VectorWritable vectorWritable : values){recommendationVector = recommendationVector == null ? vectorWritable.get() : recommendationVector.plus(vectorWritable.get());}Queue<RecommendedItem> topItems = new PriorityQueue<RecommendedItem>(recommendationsPerUser + 1, Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));Iterator<Vector.Element> recommendationVectorIterator = recommendationVector.nonZeroes().iterator();while(recommendationVectorIterator.hasNext()){Vector.Element element = recommendationVectorIterator.next();int index = element.index();float value = (float)element.get();if(topItems.size() < recommendationsPerUser){topItems.add(new GenericRecommendedItem(indexItemIDMap.get(index), value));}else if(value > topItems.peek().getValue()){topItems.add(new GenericRecommendedItem(indexItemIDMap.get(index), value));topItems.poll();}}List<RecommendedItem> recommendations = new ArrayList<RecommendedItem>(topItems.size());recommendations.addAll(topItems);Collections.sort(recommendations, ByValueRecommendedItemComparator.getInstance());context.write(key, new RecommendedItemsWritable(recommendations));}}</span></strong>