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;/**  * 基于用戶的協同過濾推薦算法實現 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 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 輸入數據 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				}			}		}		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:6Input user--items maping infermation:<eg:A a b d>aassdddjshgjh2415231424dsjkj dklsjf ladkjsfdf8g78dfg78 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>aassdd0aassdd--djshgjh相似度:NaNaassdd--2415231424相似度:NaNaassdd--dsjkj相似度:NaNaassdd--df8g78dfg78相似度:NaNaassdd--48787相似度:NaNThe item dklsjf for aassdd's recommended degree:NaNThe item sdfasd for aassdd's recommended degree:NaNThe item 8787 for aassdd's recommended degree:NaNThe item ladkjsf for aassdd's recommended degree:NaN