协同过滤算法 java源码 毕设demo

package com.bp.domain.util; import java.util.ArrayList; import java.util.Collections; import java.util.Comparator; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Map.Entry; import org.springframework.beans.factory.annotation.Autowired; import com.bp.mapper.UserDataUtilMapper; import com.bp.service.UserService; public class UserSimUtil { @Autowired private UserDataUtilMapper userDataUtilMapper; @Autowired private UserService userService; Map<String, Double> simUserSimMap = new HashMap<String, Double>(); @Autowired private UserDataUtil udUtil; //与其他用户比较，求皮尔逊相关系数 public List<String> UserSim(int u_id){ List<String> newsList=new ArrayList<String>(); String id=String.valueOf(u_id); Map<String, Map<String, Double>> userPerfMap=udUtil.getAllUsers(); Map<String, Map<String, Double>> UserObjMap=udUtil.getAllObjUsers(); String output1 = "皮尔逊相关系数:"; Map<String, Double> per=userPerfMap.get(id); for (Entry<String, Map<String, Double>> userPerfEn : userPerfMap.entrySet()) { String userId = userPerfEn.getKey(); if (!id.equals(userId)) { double sim = getUserSimilar(per, userPerfEn.getValue()); output1 += id+"与" + userId + "之间的相关系数:" + sim + ","; simUserSimMap.put(userId, sim); } } System.out.println(output1); System.out.println("根据系数推荐:"); List<String> list=getRecommend(UserObjMap, simUserSimMap,id); Map<String, Double> liMap=UserObjMap.get(id); int flag=1; for(int i=0;i<list.size();i++){ for(Entry<String, Double> entry : liMap.entrySet()){ if(entry.getKey().equals(list.get(i))){ flag=0; break; }else{ flag=1; } } if(flag==1){ newsList.add(list.get(i)); //System.out.println(); } } return newsList;//获得推荐新闻 } //推荐新闻 private List<String> getRecommend(Map<String, Map<String, Double>> simUserObjMap, Map<String, Double> simUserSimMap, String id) { Map<String, Double> objScoreMap = new HashMap<String, Double>(); for (Entry<String, Map<String, Double>> simUserEn : simUserObjMap.entrySet()) { String user = simUserEn.getKey(); double sim = 0; if(!id.equals(user)){ sim = simUserSimMap.get(user); System.out.println(user); for (Entry<String, Double> simObjEn : simUserEn.getValue().entrySet()) { double objScore = sim * simObjEn.getValue(); String objName = simObjEn.getKey(); //System.out.println(objName); if (objScoreMap.get(objName) == null) { objScoreMap.put(objName, objScore); }else { double totalScore = objScoreMap.get(objName); objScoreMap.put(objName, totalScore + objScore); } } } } List<Entry<String, Double>> enList = new ArrayList<Entry<String, Double>>(objScoreMap.entrySet()); Collections.sort(enList, new Comparator<Map.Entry<String, Double>>() { public int compare(Map.Entry<String, Double> o1, Map.Entry<String, Double> o2) { Double a = o1.getValue() - o2.getValue(); if (a == 0) { return 0; }else if (a > 0) { return 1; }else { return -1; } } }); List<String> list=new ArrayList<String>(); for(int i=enList.size()-1;i>-1;i--){ System.out.println(enList.get(i).getKey()); System.out.println(enList.get(i).getValue()); } for(int i=enList.size()-1;i>-1;i--){ if(enList.get(i).getValue()>5){ list.add(enList.get(i).getKey()); } } System.out.println(enList.size()+"个"); return list; } //计算相似度 private double getUserSimilar(Map<String, Double> pm1, Map<String, Double> pm2) { int n = 0;// 数量n int sxy = 0;// Σxy=x1*y1+x2*y2+....xn*yn int sx = 0;// Σx=x1+x2+....xn int sy = 0;// Σy=y1+y2+...yn int sx2 = 0;// Σx2=(x1)2+(x2)2+....(xn)2 int sy2 = 0;// Σy2=(y1)2+(y2)2+....(yn)2 for (Entry<String, Double> pme : pm1.entrySet()) { String key = pme.getKey(); Double x = pme.getValue(); Double y = pm2.get(key); if (x != null && y != null) { n++; sxy += x * y; sx += x; sy += y; sx2 += Math.pow(x, 2); sy2 += Math.pow(y, 2); } } // p=(Σxy-Σx*Σy/n)/Math.sqrt((Σx2-(Σx)2/n)(Σy2-(Σy)2/n)); double sd = sxy - sx * sy / n; double sm = Math.sqrt((sx2 - Math.pow(sx, 2) / n) * (sy2 - Math.pow(sy, 2) / n)); return Math.abs(sm == 0 ? 1 : sd / sm); } }