基于用户的协同过滤算法---《推荐系统实践》---Python源码（9）

一、总体说明本代码以《推荐系统实践》这本书的代码为框架，进行改写。数据集为点击打开链接中的ratings.dat数据。为了方便起见，所有代码都写在一个文件中，能够保证无需任何更改，直接执行，就能出结果
二、符号说明

参数名	类型	说明
data	list	读取ratings.dat的全部数据
M	int	将data近似分为M份，M-1为训练数据，1份为测试数据
seed	int	将data分成训练数据和测试数据函数中，随机数的种子
N	int	N个推荐结果
K	int	基于邻域算法选择K个邻居
W	dict<int,dict<int ,int>>	相似度矩阵,例如，储存用户u和用户v的相似度s，为W[u][v] = s
allrank	dict<int,list>	记录了所有用户的推荐结果。以<用户,推荐结果>的形式储存，推荐结果为一个list

三、函数说明

函数名	说明
SplitData	将data分为训练数据和测试数据
list2dic	将测试数据和训练数据的类型从list转为dict
UserSimilarity1	计算用户u和用户v的余弦相似度，计算速度未优化
Recommand	为所有用户推荐物品
Precision	计算推荐结果的准确度

四、Python源码import random as rd , math as mt, operator as op
"""
SplitData(data, M, k, seed) approximately split data into M-1 train data and 1 test data.
This function should be call M times on the conditon that K varies from 0 to M-1
and keep seed constant.

"""
def SplitData(data, M, k, seed):
test = []
train = []
rd.seed(seed)
for user,item in data:
if rd.randint(0,M) == k:#generate a uniform random number in [0,M]
test.append([user,item])
else:
train.append([user,item])
return train, test
"""
the data structure of train data, test data are express a dictionary like {key:set}.
key is user. set is a set of movies that the users have rated.
"""
def list2dic(listdata):
dicdata = dict()
for user,item in listdata:
if user not in dicdata.keys():
dicdata[user] = set()
dicdata[user].add(item)
else:
dicdata[user].add(item)
return dicdata

def UserSimilarity1(train):
W = dict()
for u in train.keys():
W[u]=dict()
for v in train.keys():
if u==v:
continue
W[u][v] = len(train[u] & train[v])
W[u][v] /= mt.sqrt(len(train[u]) * len(train[v]) * 1.0)
return W

# top-N list for user u
def Recommand(train, W, K, N):
allrank = dict()
for u in train.keys():
rank = dict()
for v, wuv in sorted(W[u].items(), key = op.itemgetter(1), reverse = True)[0:K]:
for i in train[v]:
if i not in train[u]:
if i not in rank.keys():
rank[i] = wuv;
else:
rank[i] += wuv * 1
ranklist = []
for item, sims in sorted(rank.items(), key = op.itemgetter(1), reverse = True)[0:N]:
ranklist.append(item)
allrank[u] = ranklist
return allrank
# compute precision
def Precision(allrank, test, N):
hit = 0
all = 0
for user in test.keys():
tu = test[user]
if user in allrank.keys():
for item in allrank[user]:
if item in tu:
hit+=1
all += N
return hit / (all*1.0)
"""
main function
"""
filestring = '/home/sysu-hgavin/文档/ml-1m/ratings.dat'
f = open(filestring, 'r')
data = []
while 1:
line = f.readline()#read data
if not line:
break
line = line.split("::")[:2]
line[0] = int (line[0])
line[1] = int (line[1])
data.append(line)
f.close()
M = 8
seed = 1
N = 10 #top-N
K = 20 #the number of neighbors
#for k in range(M-1):
train, test = SplitData(data, M, 1, seed)#generate train data and test data
train = list2dic(train)
test = list2dic(test)

W = UserSimilarity1(train)
allrank = Recommand(train, W, K, N)
precision = Precision(allrank, test, N)
print (precision)