锐客网

  1. 首页 > it技术 > >

基于社交网络的用户与基于物品的协同过滤推荐算法-java

完整工程+数据源：https://github.com/scnuxiaotao/recom_sys

import java.io.BufferedReader; import java.io.FileNotFoundException; import java.io.FileReader; import java.io.FileWriter; import java.io.IOException; import java.util.HashMap; public class itemcf { /* * *主函数~ * */ /*public static void main(String[] args) throws IOException { _Run(); }*/ /* * *基于物品的实现~ * */ static int usersum = 20836; //用户数 static int itemsum = 200; //物品总数 static int N = 3; //推荐个数 static int[][] train; //训练集合user item rate矩阵 static int[][] test; //测试集合user item rate矩阵 static double[][] trainuseritem; //训练集合user item 兴趣程度 矩阵 static int[][] recommend; //为每个用户推荐N个物品 static simi [][]simiItem; //排序后的相似性矩阵 static double [][]itemsim; //未排序的相似性矩阵 static String road = "data/6 总评论情感分析结果/酒店-评论（已转化）.txt"; //数据路径，格式为用户编号::物品编号::评分 static String road2 = "data/10 推荐/(已转化)天河酒店ID.txt"; static String road3 = "data/10 推荐/物品推荐.txt"; public static String road4 = "data/10 推荐/(已转化)用户ID.txt"; public static class simi { double value; //相似值 int num; //相似物品号 }; public static void _Run() throws IOException {get_user_hotel_num(); System.out.println("usersum: "+usersum); System.out.println("itemsum: "+itemsum); train = new int[itemsum][usersum]; train[0][0] = 0; //训练集合user item rate矩阵 test = new int[itemsum][usersum]; test[0][0] = 0; //测试集合user item rate矩阵 trainuseritem = new double[usersum][itemsum]; trainuseritem[0][0] = 0.0; //训练集合user item 兴趣程度 矩阵 recommend = new int[usersum][N]; recommend[0][0] = 0; //为每个用户推荐N个物品 simiItem = new simi[itemsum][itemsum]; //排序后的相似性矩阵itemsim = new double[itemsum][itemsum]; //未排序的相似性矩阵int i,j,k = 8; //去用户的k个最近邻居（相似度最高）来计算推荐物品for(i = 0 ; i < itemsum; ++i) for(j = 0 ; j < itemsum; ++j) simiItem[i][j] = new simi(); System.out.println("1.训练集"); SplitData(8,1); //输出初始化的矩阵 /*for (i=0; i<10; i++) { System.out.println("Item"+i+":"); for (j=0; j<5; j++) { System.out.print(train[i][j]+""); } System.out.println(); }*/System.out.println("2.计算物品之间相似性，得到相似性矩阵"); for (i=0; i0&&ItemB[i]>0) { comUser++; //查找ItemA与ItemB的都被用户评论的用户个数 } if (ItemA[i]>0){ countIa++; //评论ItemA的用户数量 } if (ItemB[i]>0){ countIb++; //评论ItemB的用户数量 } } double tem = Math.sqrt(countIa*countIb); //double tem = 1; //System.out.println(tem); if(tem == 0) { return 0; } else { simility = comUser/tem; return simility; } } /*物品相似性矩阵排序（根据相似性由高到低排序）*/ public static void quickSort(int x, int start, int end) { if (start < end) { double base = simiItem[x][start].value; // 选定的基准值（第一个数值作为基准值） double temp; // 记录临时中间值 int i_tmp; int i = start, j = end; do { while ((simiItem[x][i].value > base) && (i < end)) i++; while ((simiItem[x][j].value < base) && (j > start)) j--; if (i <= j) { temp = simiItem[x][i].value; simiItem[x][i].value = https://www.it610.com/article/simiItem[x][j].value; simiItem[x][j].value = temp; i_tmp = simiItem[x][i].num; simiItem[x][i].num = simiItem[x][j].num; simiItem[x][j].num = i_tmp; i++; j--; } } while (i <= j); if (start < j) quickSort(x, start, j); if (end> i) quickSort(x, i, end); } } public static int sort() { for (int i=0; i0)//若这个用户同样对相似物品也有过行为 { trainuseritem[i][j]+=simiItem[j][x].value; } } return trainuseritem[i][j]; } /*通过物品兴趣程度，推荐前N个*/ public static int getRecommend() //有bug,已修改 { int maxnum; //当前最感兴趣物品号 for(int i=0; i




package WjPack; import java.io.BufferedReader; import java.io.FileNotFoundException; import java.io.FileReader; import java.io.FileWriter; import java.io.IOException; import java.util.ArrayList; import java.util.HashMap; import java.util.HashSet; import java.util.Iterator; import java.util.Map; import java.util.Set; public class new_ojld_dis { /* * *主函数~ * */ /*public static void main(String[] args) throws IOException {run(); }*/ /* * *基于用户的实现~ * */ static String road_main = "data"; static String road = road_main + "/6 总评论情感分析结果/酒店-评论（已转化）.txt"; //数据路径，格式为用户编号::物品编号::评分 static String road2 = road_main + "/10 推荐/(已转化)用户-关注.txt"; //数据路径，格式为用户编号::关注编号 static String road3 = road_main + "/10 推荐/(已转化)天河酒店ID.txt"; static String road4 = road_main + "/10 推荐/用户推荐.txt"; static String road5 = road_main + "/10 推荐/(已转化)用户ID.txt"; static int usersum = 20836; //用户数 static int itemsum = 200; //物品总数 static Map> score = new HashMap>(); static Set userSet = new HashSet(); static Set filmSet = new HashSet(); static FileWriter txtw; static String tjhotel = ""; static ArrayList arr; static {arr = new ArrayList(); try { score = get_score_from_road(); } catch (IOException e) { } } public static void run() throws IOException { txtw=new FileWriter(road4); get_hotelid(road3); for(int m = 0; m < 20836; ++m) { tjhotel = ""; new_ojld_dis.outNearbyUserList(arr.get(m)); if(tjhotel.length() > 1) txtw.write("user"+arr.get(m)+tjhotel+"\r\n"); } txtw.close(); } public static void init() { try { get_user_hotel_num(); } catch (IOException e1) { // TODO Auto-generated catch block e1.printStackTrace(); } System.out.println(usersum); System.out.println(itemsum); } public static void get_user_hotel_num() throws IOException { FileReader data_about = new FileReader(road3); BufferedReader read_data_about=new BufferedReader(data_about); int num = 0; while(read_data_about.readLine() != null) num++; itemsum = num; data_about.close(); read_data_about.close(); data_about = new FileReader(road5); read_data_about=new BufferedReader(data_about); num = 0; while(read_data_about.readLine() != null) num++; usersum = num; data_about.close(); read_data_about.close(); } public static Map> get_score_from_road() throws IOException { init(); String []tmps = new String[5]; FileReader data_about=new FileReader(road); BufferedReader read_data_about=new BufferedReader(data_about); String s2; //暂存文件一行记录 String usertmp = null; Map> score = new HashMap>(); HashMap tempScore = new HashMap(); while((s2=read_data_about.readLine())!=null){//寻找数据集每条记录对应的用户号和物品号 tmps = s2.split("::"); String username = tmps[0]; String filmname = tmps[1]; Integer socrename = Integer.valueOf(tmps[2]); if(usertmp == null) {usertmp = username; arr.add(usertmp); } else if(!usertmp.equals(username)) { score.put(usertmp, tempScore); usertmp = username; arr.add(usertmp); tempScore = new HashMap(); }tempScore.put(filmname, socrename); } score.put(usertmp, tempScore); arr.add(usertmp); return score; }public static void outNearbyUserList(String user) throws IOException { FileReader data_about=new FileReader(road2); BufferedReader read_data_about=new BufferedReader(data_about); Map scores = new HashMap(); String []tmps = new String[5]; HashMap thing = new HashMap(); String id; //暂存文件一行记录 int num = 0; while((id=read_data_about.readLine())!=null){ tmps = id.split("::"); String username = tmps[0]; String fansname = tmps[1]; if(username.equals(user)) { thing.put(fansname, 1); }}for (int m = 0; m < arr.size()-1; ++m) { String tempUser = arr.get(m); if (tempUser.equals(user) || !thing.containsKey(tempUser)) { continue; }double score = getOSScore(user, tempUser); if(score >= 0) scores.put(tempUser, score); } data_about.close(); read_data_about.close(); }private static Double getOSScore(String user1, String user2) throws NumberFormatException, IOException { HashMap user1Score = (HashMap) score.get(user1); HashMap user2Score = (HashMap) score.get(user2); double totalscore = 100; ArrayList hobby = new ArrayList(); Iterator it = user1Score.keySet().iterator(); while (it.hasNext()) { String film = (String) it.next(); int a1 = (Integer) user1Score.get(film); //System.out.println(film); if(user2Score.get(film) == null) continue; int b1 = (Integer) user2Score.get(film); int a = a1 * a1 - b1 * b1; //System.out.println(Math.abs(a)); totalscore = Math.sqrt(Math.abs(a)); } if(totalscore == 0) { int ok = 0; it = user2Score.keySet().iterator(); if(it != null) { while (it.hasNext()) { String film = (String) it.next(); if(user1Score.get(film) == null) { if(ok == 0) { ok = 1; } tjhotel+="::"+hotel[Integer.parseInt(film)]; }}}} return totalscore; }static String []hotel = new String[201]; public static void get_hotelid(String r1) throws IOException { FileReader data_about=new FileReader(r1); BufferedReader read_data_about=new BufferedReader(data_about); String id; //暂存文件一行记录 String []tmps = new String[5]; while((id=read_data_about.readLine())!=null){ tmps = id.split("::"); String hotelname = tmps[0]; String number = tmps[1]; hotel[Integer.parseInt(number)] = hotelname; } data_about.close(); read_data_about.close(); }}


课程设计写的代码，可以用的，不过不写注释，但也不是很难看懂，先了解以下原理再看看代码就差不多了～


【基于社交网络的用户与基于物品的协同过滤推荐算法-java】PS：因为抓到的用户ID和酒店都是数据都是类似434132这么长的编号，为了方便我用数组存，事先我全部转化为1开始的编号了。比如说有两个4654654,32131321，那我就转成1,2了，处理完推荐完再把1,2转成4654654,32131321

    推荐阅读


    上一篇：家庭作业。

    下一篇：腾讯网无障碍说明