add basedItem

Author: =

Recommend/基于item的协同过滤推荐BasedItem.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+#-*-coding:utf-8-*-
+'''
+Created on 2016-5-30
+
+@author: thinkgamer
+'''
+import math
+
+class ItemBasedCF:
+    def __init__(self,train_file):
+        self.train_file = train_file
+        self.readData()
+    def readData(self):
+        #读取文件，并生成用户-物品的评分表和测试集
+        self.train = dict()     #用户-物品的评分表
+        for line in open(self.train_file):
+            # user,item,score = line.strip().split(",")
+            user,score,item = line.strip().split(",")
+            self.train.setdefault(user,{})
+            self.train[user][item] = int(float(score))
+
+    def ItemSimilarity(self):
+        #建立物品-物品的共现矩阵
+        C = dict()  #物品-物品的共现矩阵
+        N = dict()  #物品被多少个不同用户购买
+        for user,items in self.train.items():
+            for i in items.keys():
+                N.setdefault(i,0)
+                N[i] += 1
+                C.setdefault(i,{})
+                for j in items.keys():
+                    if i == j : continue
+                    C[i].setdefault(j,0)
+                    C[i][j] += 1
+        #计算相似度矩阵
+        self.W = dict()
+        for i,related_items in C.items():
+            self.W.setdefault(i,{})
+            for j,cij in related_items.items():
+                self.W[i][j] = cij / (math.sqrt(N[i] * N[j]))
+        return self.W
+
+    #给用户user推荐，前K个相关用户
+    def Recommend(self,user,K=3,N=10):
+        rank = dict()
+        action_item = self.train[user]     #用户user产生过行为的item和评分
+        for item,score in action_item.items():
+            for j,wj in sorted(self.W[item].items(),key=lambda x:x[1],reverse=True)[0:K]:
+                if j in action_item.keys():
+                    continue
+                rank.setdefault(j,0)
+                rank[j] += score * wj
+        return dict(sorted(rank.items(),key=lambda x:x[1],reverse=True)[0:N])
+    
+#声明一个ItemBased推荐的对象    
+Item = ItemBasedCF("uid_score_bid")
+Item.ItemSimilarity()
+recommedDic = Item.Recommend("xiyuweilan")
+for k,v in recommedDic.iteritems():
+    print k,"\t",v