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;
/** 
 * 基于用戶的協(xié)同過濾推薦算法實現(xiàn) 
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) {
		/** 
   * 輸入用戶-->物品條目 一個用戶對應多個物品 
   * 用戶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:");
		//輸入用戶總量 
		int N = scanner.nextint();
		int[][] sparseMatrix = new int[N][N];
		//建立用戶稀疏矩陣，用于用戶相似度計算【相似度矩陣】 
		Map<String, Integer> userItemLength = new HashMap<>();
		//存儲每一個用戶對應的不同物品總數(shù) eg: A 3 
		Map<String, Set<String>> itemUserCollection = new HashMap<>();
		//建立物品到用戶的倒排表 eg: a A B 
		Set<String> items = new HashSet<>();
		//輔助存儲物品集合 
		Map<String, Integer> userID = new HashMap<>();
		//輔助存儲每一個用戶的用戶ID映射 
		Map<Integer, String> idUser = new HashMap<>();
		//輔助存儲每一個ID對應的用戶映射 
		System.out.println("Input user--items maping infermation:<eg:A a b d>");
		scanner.nextLine();
		for (int i = 0; i < N ; i++){
			//依次處理N個用戶 輸入數(shù)據(jù) 以空格間隔 
			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);
			//用戶ID與稀疏矩陣建立對應關系 
			idUser.put(i, user_item[0]);
			//建立物品--用戶倒排表 
			for (int j = 1; j < length; j ++){
				if(items.contains(user_item[j])){
					//如果已經(jīng)包含對應的物品--用戶映射，直接添加對應的用戶 
					itemUserCollection.get(user_item[j]).add(user_item[0]);
				} else{
					//否則創(chuàng)建對應物品--用戶集合映射 
					items.add(user_item[j]);
					itemUserCollection.put(user_item[j], new HashSet<String>());
					//創(chuàng)建物品--用戶倒排關系 
					itemUserCollection.get(user_item[j]).add(user_item[0]);
				}
			}
		}
		System.out.println(itemUserCollection.toString());
		//計算相似度矩陣【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;
					//計算用戶u與用戶v都有正反饋的物品總數(shù)
				}
			}
		}
		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));
		//計算用戶之間的相似度【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))));
			}
		}
		//計算指定用戶recommendUser的物品推薦度 
		for (String item: items){
			//遍歷每一件物品 
			Set<String> users = itemUserCollection.get(item);
			//得到購買當前物品的所有用戶集合 
			if(!users.contains(recommendUser)){
				//如果被推薦用戶沒有購買當前物品，則進行推薦度計算 
				double itemRecommendDegree = 0.0;
				for (String user: users){
					itemRecommendDegree += sparseMatrix[userID.get(recommendUser)][userID.get(user)]/Math.sqrt(userItemLength.get(recommendUser)*userItemLength.get(user));
					//推薦度計算
				}
				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