推荐系统项目实战-电影推荐系统
推荐系统项目实战
强烈推荐按这本书哦，资料很全，也很有逻辑
新的一年，学习新的知识，这里学习了这本书，计划两周学完
- 数据集 链接：https://pan.baidu.com/s/1MVsdKM2q6cq-mL_I5DOt7A
提取码：0tqo
- 代码
# -*- coding:utf-8 -*-
"""
Author: Thinkgamer
Desc:
代码2-1 实例1：搭建你的第一个推荐系统-电影推荐系统
从中随机选择1000个与用户进行计算
"""
import os
import json
import random
import math
class FirstRec:
"""
初始化函数
filePath: 原始文件路径
seed：产生随机数的种子
k：选取的近邻用户个数
nitems：为每个用户推荐的电影数
"""
def __init__(self,file_path,seed,k,n_items):
self.file_path = file_path
self.users_1000 = self.__select_1000_users()
self.seed = seed
self.k = k
self.n_items = n_items
self.train,self.test = self._load_and_split_data()
# 获取所有用户并随机选取1000个
def __select_1000_users(self):
print("随机选取1000个用户！")
if os.path.exists("data/train.json") and os.path.exists("data/test.json"):
return list()
else:
users = set()
# 获取所有用户
for file in os.listdir(self.file_path):
one_path = "{}/{}".format(self.file_path, file)
print("{}".format(one_path))
with open(one_path, "r") as fp:
for line in fp.readlines():
if line.strip().endswith(":"):
continue
userID, _ , _ = line.split(",")
users.add(userID)
# 随机选取1000个
users_1000 = random.sample(list(users),1000)
print(users_1000)
return users_1000
# 加载数据，并拆分为训练集和测试集
def _load_and_split_data(self):
train = dict()
test = dict()
if os.path.exists("data/train.json") and os.path.exists("data/test.json"):
print("从文件中加载训练集和测试集")
train = json.load(open("data/train.json"))
test = json.load(open("data/test.json"))
print("从文件中加载数据完成")
else:
# 设置产生随机数的种子，保证每次实验产生的随机结果一致
random.seed(self.seed)
for file in os.listdir(self.file_path):
one_path = "{}/{}".format(self.file_path, file)
print("{}".format(one_path))
with open(one_path,"r") as fp:
movieID = fp.readline().split(":")[0]
for line in fp.readlines():
if line.endswith(":"):
continue
userID, rate, _ = line.split(",")
# 判断用户是否在所选择的1000个用户中
if userID in self.users_1000:
if random.randint(1,50) == 1:
test.setdefault(userID, {
})[movieID] = int(rate)
else:
train.setdefault(userID, {
})[movieID] = int(rate)
print("加载数据到 data/train.json 和 data/test.json")
json.dump(train,open("data/train.json","w"))
json.dump(test,open("data/test.json","w"))
print("加载数据完成")
return train,test
"""
计算皮尔逊相关系数
rating1：用户1的评分记录，形式如{"movieid1":rate1,"movieid2":rate2,...}
rating2：用户1的评分记录，形式如{"movieid1":rate1,"movieid2":rate2,...}
"""
def pearson(self,rating1,rating2):
sum_xy = 0
sum_x = 0
sum_y = 0
sum_x2 = 0
sum_y2 = 0
num = 0
for key in rating1.keys():
if key in rating2.keys():
num += 1
x = rating1[key]
y = rating2[key]
sum_xy += x * y
sum_x += x
sum_y += y
sum_x2 += math.pow(x,2)
sum_y2 += math.pow(y,2)
if num == 0:
return 0
# 皮尔逊相关系数分母
denominator = math.sqrt( sum_x2 - math.pow(sum_x,2) / num) * math.sqrt( sum_y2 - math.pow(sum_y,2) / num )
if denominator == 0:
return 0
else:
return ( sum_xy - ( sum_x * sum_y ) / num ) / denominator
"""
用户userID进行电影推荐
userID：用户ID
"""
def recommend(self,userID):
neighborUser = dict()
for user in self.train.keys():
if userID != user:
distance = self.pearson(self.train[userID],self.train[user])
neighborUser[user]=distance
# 字典排序
newNU = sorted(neighborUser.items(),key = lambda k:k[1] ,reverse=True)
movies = dict()
for (sim_user,sim) in newNU[:self.k]:
for movieID in self.train[sim_user].keys():
movies.setdefault(movieID,0)
movies[movieID] += sim * self.train[sim_user][movieID]
newMovies = sorted(movies.items(), key = lambda k:k[1], reverse=True)
return newMovies
"""
推荐系统效果评估函数
num: 随机抽取 num 个用户计算准确率
"""
def evaluate(self,num=30):
print("开始计算准确率")
precisions = list()
random.seed(10)
for userID in random.sample(self.test.keys(),num):
hit = 0
result = self.recommend(userID)[:self.n_items]
for (item,rate) in result:
if item in self.test[userID]:
hit += 1
precisions.append(hit/self.n_items)
return sum(precisions) / precisions.__len__()
# main函数，程序的入口
if __name__ == "__main__":
file_path = "data/netflix/training_set"
seed = 30
k = 15
n_items =20
f_rec = FirstRec(file_path,seed,k,n_items)
# 计算用户 195100 和 1547579的皮尔逊相关系数
r = f_rec.pearson(f_rec.train["195100"],f_rec.train["1547579"])
print("195100 和 1547579的皮尔逊相关系数为：{}".format(r))
# 为用户195100进行电影推荐
result = f_rec.recommend("195100")
print("为用户ID为：195100的用户推荐的电影为：{}".format(result))
print("算法的推荐准确率为: {}".format(f_rec.evaluate()))
- 结果
随机选取1000个用户！
从文件中加载训练集和测试集
从文件中加载数据完成
195100 和 1547579的皮尔逊相关系数为：0.1194695382178992
为用户ID为：195100的用户推荐的电影为：[('3938', 22.0), ('14538', 19.000000000000004), ('14103', 19.0), ('15205', 18.000000000000004), ('17355', 18.0), ('1905', 18.0), ('12317', 16.000000000000004), ('13255', 16.000000000000004), ('5317', 14.000000000000004), ('11283', 14.0), ('14240', 14.0), ('6974', 14.0), ('16265', 14.0), ('6206', 14.0), ('11521', 14.0), ('1145', 13.000000000000005), ('17169', 13.000000000000005), ('9340', 13.000000000000004), ('4306', 13.0), ('11132', 13.0), ('17324', 13.0), ('14313', 12.000000000000002), ('16879', 12.0), ('3917', 12.0), ('7624', 12.0), ('8644', 12.0), ('13593', 12.0), ('6844', 11.000000000000002), ('758', 11.0), ('313', 11.0), ('8393', 11.0), ('11089', 11.0), ('13050', 11.0), ('14454', 11.0), ('16882', 11.0), ('12911', 10.000000000000005), ('15582', 10.000000000000005), ('30', 10.0), ('14621', 10.0), ('16377', 10.0), ('5582', 10.0), ('9628', 10.0), ('3274', 10.0), ('5496', 10.0), ('16082', 10.0), ('10550', 9.999999999999998), ('1220', 9.999999999999998), ('1804', 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('17129', 3.0), ('17175', 3.0), ('17381', 3.0), ('16113', 3.0), ('11681', 3.0), ('15641', 3.0), ('1138', 3.0), ('5793', 3.0), ('5828', 3.0), ('5836', 3.0), ('6860', 3.0), ('7184', 3.0), ('7281', 3.0), ('8295', 3.0), ('10860', 3.0), ('11931', 3.0), ('12322', 3.0), ('14113', 3.0), ('15764', 3.0), ('312', 2.999999999999999), ('1283', 2.999999999999999), ('2779', 2.999999999999999), ('2958', 2.999999999999999), ('3151', 2.999999999999999), ('4493', 2.999999999999999), ('4695', 2.999999999999999), ('6497', 2.999999999999999), ('7238', 2.999999999999999), ('7971', 2.999999999999999), ('9415', 2.999999999999999), ('9442', 2.999999999999999), ('10773', 2.999999999999999), ('13061', 2.999999999999999), ('13214', 2.999999999999999), ('14890', 2.999999999999999), ('14940', 2.999999999999999), ('15343', 2.999999999999999), ('17062', 2.999999999999999), ('17111', 2.999999999999999), ('9645', 2.999999999999999), ('15034', 2.999999999999999), ('963', 2.999999999999999), ('1464', 2.999999999999999), ('406', 2.999999999999999), ('442', 2.999999999999999), ('2172', 2.999999999999999), ('2942', 2.999999999999999), ('4877', 2.999999999999999), ('5154', 2.999999999999999), ('7739', 2.999999999999999), ('8535', 2.999999999999999), ('10375', 2.999999999999999), ('11047', 2.999999999999999), ('11090', 2.999999999999999), ('11696', 2.999999999999999), ('13736', 2.999999999999999), ('15471', 2.999999999999999), ('305', 2.999999999999999), ('1307', 2.999999999999999), ('10101', 2.999999999999999), ('12303', 2.999999999999999), ('28', 2.0), ('6720', 2.0), ('12774', 2.0), ('15474', 2.0), ('1700', 2.0), ('2226', 2.0), ('16095', 2.0), ('17345', 2.0), ('1068', 2.0), ('11170', 2.0), ('6255', 2.0), ('8418', 2.0), ('17031', 2.0), ('17251', 2.0), ('331', 2.0), ('2477', 2.0), ('7249', 2.0), ('10947', 2.0), ('13519', 2.0), ('16640', 2.0), ('16859', 2.0), ('468', 1.9999999999999996), ('2856', 1.9999999999999996), ('4733', 1.9999999999999996), ('6084', 1.9999999999999996), ('8824', 1.9999999999999996), ('10078', 1.9999999999999996), ('13565', 1.9999999999999996), ('13855', 1.9999999999999996), ('14440', 1.9999999999999996), ('14898', 1.9999999999999996), ('15608', 1.9999999999999996), ('16603', 1.9999999999999996), ('16730', 1.9999999999999996), ('17704', 1.9999999999999996), ('9800', 1.9999999999999996), ('658', 1.9999999999999996), ('2391', 1.9999999999999996), ('2486', 1.9999999999999996), ('5837', 1.9999999999999996), ('10775', 1.9999999999999996), ('15777', 1.9999999999999996), ('3314', 1.9999999999999996), ('4590', 1.9999999999999996), ('7521', 1.9999999999999996), ('11065', 1.9999999999999996), ('13043', 1.9999999999999996), ('14389', 1.9999999999999996), ('17387', 1.000000000000001), ('1975', 1.0), ('2361', 1.0), ('4103', 1.0), ('5725', 1.0), ('16145', 1.0), ('191', 1.0), ('6975', 1.0), ('14332', 1.0), ('3713', 1.0), ('7904', 1.0), ('5991', 1.0), ('6596', 1.0), ('1012', 0.9999999999999998), ('2939', 0.9999999999999998), ('7780', 0.9999999999999998), ('3161', 0.9999999999999998), ('13471', 0.9999999999999998), ('14154', 0.9999999999999998)]
开始计算准确率
算法的推荐准确率为: 0.005000000000000001
- 总结
只是抽取的1000个训练，结果并不是很理想，全部训练集基数大，估计可行
后期有时间放上GPU结果