协同过滤包括基于物品的协同过滤和基于用户的协同过滤，本文基于电影评分数据做基于用户的推荐
主要做三个部分：1、读取数据；2、构建用户与用户的相似度矩阵；3、进行推荐；
查看数据u.data
主要用到前3列分别指 用户编号user_id、电影编号item_id、用户对电影的打分score
这个文件构建item-用户的倒排表用于构建用户和用户的相似度矩阵，构建用户-item的倒排表用于推荐
ubuntu@ubuntu-2:~/workspace/jupyter_project/recommendation$ head ./data/u.data 196 242 3 881250949 186 302 3 891717742 22 377 1 878887116 244 51 2 880606923 166 346 1 886397596 298 474 4 884182806 115 265 2 881171488 253 465 5 891628467 305 451 3 886324817 6 86 3 883603013
查看数据u.item
主要用到前两列：第一列是电影id item_id 第二列是电影名称
这个文件主要用于推荐结果展示
ubuntu@ubuntu-2:~/workspace/jupyter_project/recommendation$ head ./data/u.item 1|Toy Story (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)|0|0|0|1|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0 2|GoldenEye (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?GoldenEye%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0 3|Four Rooms (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0 4|Get Shorty (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)|0|1|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0 5|Copycat (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Copycat%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0 6|Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)|01-Jan-1995||http://us.imdb.com/Title?Yao+a+yao+yao+dao+waipo+qiao+(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0 7|Twelve Monkeys (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Twelve%20Monkeys%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0 8|Babe (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Babe%20(1995)|0|0|0|0|1|1|0|0|1|0|0|0|0|0|0|0|0|0|0 9|Dead Man Walking (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Dead%20Man%20Walking%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0 10|Richard III (1995)|22-Jan-1996||http://us.imdb.com/M/title-exact?Richard%20III%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
代码如下
# coding: utf-8
# In[64]:
#读取数据
def read_data(udata,uitem):
user_movies = {}#item - > user 用于构建相似度矩阵
user_item = {}#user -> item ->score 最后用于推荐
movies = {}
for line in open(udata):
user,item,score = line.split("\t")[:3]
user_movies.setdefault(item,{})
user_movies[item][user] = int(score)
user_item.setdefault(user,{})
user_item[user][item]= int(score)
for line in open(uitem,encoding = "ISO-8859-1"):
item,name = line.split("|")[:2]
movies.setdefault(item)
movies[item] = name
return user_movies,movies,user_item
# user_movies,movies,user_item = read_data("./data/u.data","./data/u.item")
# In[62]:
import math
#建立用户相似度矩阵
def user_similarity(user_movies):
C ={}#用于存放相似度矩阵
N = {}#用于存放每个人评价的电影数
for item , user_score in user_movies.items():
for user in user_score.keys():
N.setdefault(user,0)
N[user] += 1
C.setdefault(user,{})
for user2 in user_score.keys():
if user == user2:
continue
C[user].setdefault(user2,0)
C[user][user2] +=1
W = {}#存放最终的相似度矩阵
for user,user_score in C.items():
W.setdefault(user,{})
for user2,score in user_score.items():
W[user][user2] = C[user][user2]/math.sqrt(N[user]*N[user])
return W
# W=user_similarity(user_movies)
# In[63]:
#
def Recommend(user,user_item,W,N,M):
rank = {} #存放推荐计算结果
user=user
#N 用户相关性最大的前N个用户；
#M代表推荐最终的M个结果
for user2,w_score in sorted(W[user].items(),key = lambda x:x[1],reverse = True)[:N]:
for item,score in sorted(user_item[user2].items()):
if item in user_item[user].keys():
continue
rank.setdefault(item,{})
rank[item] = w_score*math.log(score)
return sorted(rank.items(),key = lambda x:x[1],reverse = True)[:M]
# In[65]:
if __name__ == "__main__":
print ("#导入数据")
user_movies,movies,user_item = read_data("./data/u.data","./data/u.item")
print("#计算相似度矩阵")
W = user_similarity(user_movies)
print ("#计算推荐结果")
result = Recommend(",user_item,W,2,10)
print ("#结果展示")
print ("你可能会喜欢")
for line in result:
print (movies[line[0]])