基于物品的协同过滤item-CF 之电影推荐 python

推荐算法有基于协同的Collaboration Filtering：包括 user Based和item Based；基于内容 : Content Based

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商品的推荐

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

这个文件主要用户构建物品的相似度矩阵

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

这个文件主要用于根据预测的推荐结果进行展示

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

#-*-encoding = utf-8 -*-
import math#读取数据 构建dic[每个人][看过的电影]=分数
def read_data(udata,uitem):
    user_movies={}
    movies={}
    for line in open(udata):
        user_id,movie_id,score = line.split("\t")[0:3]
        user_movies.setdefault(user_id,{})
        user_movies[user_id][movie_id] = int(score)
    for line in open(uitem,encoding = "ISO-8859-1"):
        movie_id,movie_name = line.split("|")[:2]
        movies[movie_id] = movie_name
    return user_movies,movies

#依据dict[每个人][看过的电影] = 分数 构建物品相似度矩阵
def item_similarity(user_movies,k=0):
    user_movies = user_movies
    C={}#存放最终的物品相似度矩阵
    N={}#存放每个电影的评分人数
    for user,item in user_movies.items():
        #print (user,"************************************")
        #print (item,"///////////////////")
        for i in item.keys():
            N.setdefault(i,0)
            N[i]+=1
            C.setdefault(i,{})
            for j in item.keys():
                if i == j :
                    continue
                C[i].setdefault(j,0)
                C[i][j]+=1
    W = {}
    #存放最终的物品余弦相似度矩阵
    for i,related_items in C.items():
        W.setdefault(i,{})
        for j,cij in related_items.items():
            W[i][j] = cij/(math.sqrt(N[i] * N[j]))
    return W

#计算推荐结果 K代表取每一个
def Recommend(user,user_movies,W,K,N):
    rank = {} #存放推荐计算结果
    # action_item = user_movies[user] #存放用户看过的电影,及打分
    action_item = user_movies[user]
    for item,score in action_item.items():
        for j,wj in sorted(W[item].items(),key = lambda x:x[1],reverse=True)[0:5]:
        #j代表用户每一个电影的电影推荐，依据打分的倒排推荐 wj为分数
            if j in action_item: #过滤掉推荐中看过的
                continue
            rank.setdefault(j,0)
            rank[j] += float(score*wj) #每一个电影推荐的分数是  电影用户打分*矩阵相似分数
    return dict(sorted(rank.items(),key = lambda x:x[1],reverse=True)[0:N])
if __name__ == "__main__":#主函数
    #加载数据
    user_movies,movies =read_data("./data/u.data","./data/u.item")
    #计算电影相似度
    W=item_similarity(user_movies)
    #print (W)
    #计算推荐结果
    result = Recommend(",user_movies,W,5,5)
    for i,rating in result.items():
         print (movies[i],rating)