基于用户的协同过滤电影推荐user-CF python

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]])