JAVA基于用户的协同过滤实现_JAVA_编程开发_程序员俱乐部

class="java" name="code">package recommendAlgo;

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;

public class testRecommend {

    public static void main(String[] args) {
	Map<String, Map<String, Integer>> userPerfMap = new HashMap<String, Map<String, Integer>>();
	Map<String, Integer> pref1 = new HashMap<String, Integer>();
	pref1.put("A", 3);
	pref1.put("B", 4);
	pref1.put("C", 3);
	pref1.put("D", 5);
	pref1.put("E", 1);
	pref1.put("F", 4);
	userPerfMap.put("p1", pref1);
	Map<String, Integer> pref2 = new HashMap<String, Integer>();
	pref2.put("A", 2);
	pref2.put("B", 4);
	pref2.put("C", 4);
	pref2.put("D", 5);
	pref2.put("E", 3);
	pref2.put("F", 2);
	userPerfMap.put("p2", pref2);
	Map<String, Integer> pref3 = new HashMap<String, Integer>();
	pref3.put("A", 3);
	pref3.put("B", 5);
	pref3.put("C", 4);
	pref3.put("D", 5);
	pref3.put("E", 2);
	pref3.put("F", 1);
	userPerfMap.put("p3", pref3);
	Map<String, Integer> pref4 = new HashMap<String, Integer>();
	pref4.put("A", 2);
	pref4.put("B", 2);
	pref4.put("C", 3);
	pref4.put("D", 4);
	pref4.put("E", 3);
	pref4.put("F", 2);
	userPerfMap.put("p4", pref4);
	Map<String, Integer> pref5 = new HashMap<String, Integer>();
	pref5.put("A", 4);
	pref5.put("B", 4);
	pref5.put("C", 4);
	pref5.put("D", 5);
	pref5.put("E", 1);
	pref5.put("F", 0);
	userPerfMap.put("p5", pref5);
	Map<String, Double> simUserSimMap = new HashMap<String, Double>();
	String output1 = "皮尔逊相关系数:", output2 = "欧几里得距离:";
	for (Entry<String, Map<String, Integer>> userPerfEn : userPerfMap.entrySet()) {
	    String userName = userPerfEn.getKey();
	    if (!"p5".equals(userName)) {
		double sim = getUserSimilar(pref5, userPerfEn.getValue());
		double distance = getEuclidDistance(pref5, userPerfEn.getValue());
		output1 += "p5与" + userName + "之间的相关系数:" + sim + ",";
		output2 += "p5与" + userName + "之间的距离:" + distance + ",";
		simUserSimMap.put(userName, sim);
	    }
	}
	System.out.println(output1);
	System.out.println(output2);
	Map<String, Map<String, Integer>> simUserObjMap = new HashMap<String, Map<String, Integer>>();
	Map<String, Integer> pobjMap1 = new HashMap<String, Integer>();
	pobjMap1.put("一夜惊喜", 3);
	pobjMap1.put("环太平洋", 4);
	pobjMap1.put("变形金刚", 3);
	simUserObjMap.put("p1", pobjMap1);
	Map<String, Integer> pobjMap2 = new HashMap<String, Integer>();
	pobjMap2.put("一夜惊喜", 5);
	pobjMap2.put("环太平洋", 1);
	pobjMap2.put("变形金刚", 2);
	simUserObjMap.put("p2", pobjMap2);
	Map<String, Integer> pobjMap3 = new HashMap<String, Integer>();
	pobjMap3.put("一夜惊喜", 2);
	pobjMap3.put("环太平洋", 5);
	pobjMap3.put("变形金刚", 5);
	simUserObjMap.put("p3", pobjMap3);
	System.out.println("根据系数推荐:" + getRecommend(simUserObjMap, simUserSimMap));
    }

    /**
     * 
     * @Title getUserSimilar
     * @Class testRecommend
     * @return double
     * @param pm1
     * @param pm2
     * @return
     * @Description获取两个用户之间的皮尔逊相似度,相关系数的绝对值越大,相关度越大
     * @author qinshijiang
     * @Date 2013-9-4
     */
    public static double getUserSimilar(Map<String, Integer> pm1, Map<String, Integer> 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, Integer> pme : pm1.entrySet()) {
	    String key = pme.getKey();
	    Integer x = pme.getValue();
	    Integer 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);
    }

    /**
     * 
     * @Title getEuclidDistance
     * @Class testRecommend
     * @return double
     * @param pm1
     * @param pm2
     * @return
     * @Description获取两个用户之间的欧几里得距离,距离越小越好
     * @author qinshijiang
     * @Date 2013-9-4
     */
    public static double getEuclidDistance(Map<String, Integer> pm1, Map<String, Integer> pm2) {
	double totalscore = 0.0;
	for (Entry<String, Integer> test : pm1.entrySet()) {
	    String key = test.getKey();
	    Integer a1 = pm1.get(key);
	    Integer b1 = pm2.get(key);
	    if (a1 != null && b1 != null) {
		double a = Math.pow(a1 - b1, 2);
		totalscore += Math.abs(a);
	    }
	}
	return Math.sqrt(totalscore);
    }

    /**
     * 
     * @Title getRecommend
     * @Class testRecommend
     * @return String
     * @param simUserObjMap
     * @param simUserSimMap
     * @return
     * @Description根据相关系数得到推荐物品
     * @author qinshijiang
     * @Date 2013-9-4
     */
    public static String getRecommend(Map<String, Map<String, Integer>> simUserObjMap, Map<String, Double> simUserSimMap) {
	Map<String, Double> objScoreMap = new HashMap<String, Double>();
	for (Entry<String, Map<String, Integer>> simUserEn : simUserObjMap.entrySet()) {
	    String user = simUserEn.getKey();
	    double sim = simUserSimMap.get(user);
	    for (Entry<String, Integer> simObjEn : simUserEn.getValue().entrySet()) {
		double objScore = sim * simObjEn.getValue();
		String objName = simObjEn.getKey();
		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;
		}
	    }
	});
	return enList.get(enList.size() - 1).getKey();
    }
}

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