package cn.csu.CFUtils;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Scanner;
import java.util.Set;
/** 
 * 基于用户的协同过滤推荐算法实现 
A a b d 
B a c 
C b e 
D c d e 
 * @author Administrator 
 * 
 */
public class UserCF {
 public static void main(String[] args) {
  /** 
   * 输入用户-->物品条目 一个用户对应多个物品 
   * 用户ID 物品ID集合 
   * A  a b d 
   * B  a c 
   * C  b e 
   * D  c d e 
   */
  Scanner scanner = new Scanner(System.in);
  System.out.println("Input the total users number:");
  //输入用户总量 
  int N = scanner.nextint();
  int[][] sparseMatrix = new int[N][N];
  //建立用户稀疏矩阵，用于用户相似度计算【相似度矩阵】 
  Map<String, Integer> userItemLength = new HashMap<>();
  //存储每一个用户对应的不同物品总数 eg: A 3 
  Map<String, Set<String>> itemUserCollection = new HashMap<>();
  //建立物品到用户的倒排表 eg: a A B 
  Set<String> items = new HashSet<>();
  //辅助存储物品集合 
  Map<String, Integer> userID = new HashMap<>();
  //辅助存储每一个用户的用户ID映射 
  Map<Integer, String> idUser = new HashMap<>();
  //辅助存储每一个ID对应的用户映射 
  System.out.println("Input user--items maping infermation:<eg:A a b d>");
  scanner.nextLine();
  for (int i = 0; i < N ; i++){
   //依次处理N个用户 输入数据 以空格间隔 
   String[] user_item = scanner.nextLine().split(" ");
   int length = user_item.length;
   userItemLength.put(user_item[0], length-1);
   //eg: A 3 
   userID.put(user_item[0], i);
   //用户ID与稀疏矩阵建立对应关系 
   idUser.put(i, user_item[0]);
   //建立物品--用户倒排表 
   for (int j = 1; j < length; j ++){
    if(items.contains(user_item[j])){
     //如果已经包含对应的物品--用户映射，直接添加对应的用户 
     itemUserCollection.get(user_item[j]).add(user_item[0]);
    } else{
     //否则创建对应物品--用户集合映射 
     items.add(user_item[j]);
     itemUserCollection.put(user_item[j], new HashSet<String>());
     //创建物品--用户倒排关系 
     itemUserCollection.get(user_item[j]).add(user_item[0]);
    }
   }
  }
  System.out.println(itemUserCollection.toString());
  //计算相似度矩阵【稀疏】 
  Set<Entry<String, Set<String>>> entrySet = itemUserCollection.entrySet();
  Iterator<Entry<String, Set<String>>> iterator = entrySet.iterator();
  while(iterator.hasNext()){
   Set<String> commonUsers = iterator.next().getValue();
   for (String user_u : commonUsers) {
    for (String user_v : commonUsers) {
     if(user_u.equals(user_v)){
      continue;
     }
     sparseMatrix[userID.get(user_u)][userID.get(user_v)] += 1;
     //计算用户u与用户v都有正反馈的物品总数
    }
   }
  }
  System.out.println(userItemLength.toString());
  System.out.println("Input the user for recommendation:<eg:A>");
  String recommendUser = scanner.nextLine();
  System.out.println(userID.get(recommendUser));
  //计算用户之间的相似度【余弦相似性】 
  int recommendUserId = userID.get(recommendUser);
  for (int j = 0;j < sparseMatrix.length; j++) {
   if(j != recommendUserId){
    System.out.println(idUser.get(recommendUserId)+"--"+idUser.get(j)+"相似度:"+sparseMatrix[recommendUserId][j]/Math.sqrt(userItemLength.get(idUser.get(recommendUserId))*userItemLength.get(idUser.get(j))));
   }
  }
  //计算指定用户recommendUser的物品推荐度 
  for (String item: items){
   //遍历每一件物品 
   Set<String> users = itemUserCollection.get(item);
   //得到购买当前物品的所有用户集合 
   if(!users.contains(recommendUser)){
    //如果被推荐用户没有购买当前物品，则进行推荐度计算 
    double itemRecommendDegree = 0.0;
    for (String user: users){
     itemRecommendDegree += sparseMatrix[userID.get(recommendUser)][userID.get(user)]/Math.sqrt(userItemLength.get(recommendUser)*userItemLength.get(user));
     //推荐度计算
    }
    System.out.println("The item "+item+" for "+recommendUser +"'s recommended degree:"+itemRecommendDegree);
   }
  }
  scanner.close();
 }
}

Input the total users number:
6
Input user--items maping infermation:<eg:A a b d>
aassdd
djshgjh
2415231424
dsjkj dklsjf ladkjsf
df8g78dfg78 8787 
48787 sdfasd
{dklsjf=[dsjkj], sdfasd=[48787], 8787=[df8g78dfg78], ladkjsf=[dsjkj]}
{aassdd=0, df8g78dfg78=1, 48787=1, 2415231424=0, djshgjh=0, dsjkj=2}
Input the user for recommendation:<eg:A>
aassdd
0
aassdd--djshgjh相似度:NaN
aassdd--2415231424相似度:NaN
aassdd--dsjkj相似度:NaN
aassdd--df8g78dfg78相似度:NaN
aassdd--48787相似度:NaN
The item dklsjf for aassdd's recommended degree:NaN
The item sdfasd for aassdd's recommended degree:NaN
The item 8787 for aassdd's recommended degree:NaN
The item ladkjsf for aassdd's recommended degree:NaN