package org.andy.mymahout.recommendation.job;import java.io.BufferedReader;import java.io.File;import java.io.FileReader;import java.io.IOException;import java.util.HashMap;import java.util.HashSet;import java.util.List;import java.util.Map;import java.util.Set;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.eval.RecommenderBuilder;import org.apache.mahout.cf.taste.impl.common.FastIDSet;import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.recommender.IDRescorer;import org.apache.mahout.cf.taste.recommender.RecommendedItem;public class RecommenderFilterSalaryResult {    final static int NEIGHBORHOOD_NUM = 2;    final static int RECOMMENDER_NUM = 3;    public static void main(String[] args) throws TasteException, IOException {        String file = "datafile/job/pv.csv";        DataModel dataModel = RecommendFactory.buildDataModelNoPref(file);        RecommenderBuilder rb1 = RecommenderEvaluator.userCityBlock(dataModel);        RecommenderBuilder rb2 = RecommenderEvaluator.itemLoglikelihood(dataModel);        Map
    
      averSalary = getAverSalary("datafile/job/job.csv", dataModel);                LongPrimitiveIterator iter = dataModel.getUserIDs();        while (iter.hasNext()) {            long uid = iter.nextLong();            System.out.print("userCityBlock    =>");            filterSalary(uid, rb1, dataModel, averSalary);            System.out.print("itemLoglikelihood=>");            filterSalary(uid, rb2, dataModel, averSalary);        }    }    public static void filterSalary(long uid, RecommenderBuilder recommenderBuilder, DataModel dataModel, Map
     
       averSalary) throws TasteException, IOException {            Set
      
        jobids = getSalaryJobID(uid, "datafile/job/job.csv", averSalary);        IDRescorer rescorer = new JobRescorer(jobids);        List
       
         list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM, rescorer);        RecommendFactory.showItems(uid, list, false);    }    public static Set
        
          getSalaryJobID(long uid, String file, Map
         
           averSalary) throws IOException { BufferedReader br = new BufferedReader(new FileReader(new File(file))); Set
          
            jobids = new HashSet
           
            (); String s = null; while ((s = br.readLine()) != null) { String[] cols = s.split(","); double salary = Double.valueOf(cols[2]); if (salary < averSalary.get(uid)) { jobids.add(Long.parseLong(cols[0])); } } br.close(); return jobids; } // 获取每个用户的基准比较工资：aver(浏览过的工资)*0.8 public static Map
            
              getAverSalary(String file, DataModel dataModel) throws NumberFormatException, IOException, TasteException{ Map
             
               salaries = new HashMap
              
               (); BufferedReader br = new BufferedReader(new FileReader(new File(file))); String s = null; while ((s = br.readLine()) != null) { String[] cols = s.split(","); salaries.put(Long.parseLong(cols[0]), Double.valueOf(cols[2])); } br.close(); Map
               
                 averSalaries = new HashMap
                
                 (); LongPrimitiveIterator iter = dataModel.getUserIDs(); while (iter.hasNext()) { long uid = iter.nextLong(); FastIDSet items = dataModel.getItemIDsFromUser(uid); LongPrimitiveIterator itemsIter = items.iterator(); double sum = 0; int count = 0; double aver = 0.0; while (itemsIter.hasNext()) { long item = itemsIter.nextLong(); double salary = salaries.get(item); sum += salary; count ++; } if(count > 0) aver = 0.8*sum/count; averSalaries.put(uid, aver); } return averSalaries; }}