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
上面链接失效了
但是我找不到之前的了
所以这里附上所有的资料，大家自行查找！

链接：https://pan.baidu.com/s/1pSHeDZQpqSkVmq3xg1OrHw
提取码：1234

代码

# -*- coding:utf-8 -*-"""Author: ThinkgamerDesc: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 mathclass 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_pathself.users_1000 = self.__select_1000_users()self.seed = seedself.k = kself.n_items = n_itemsself.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(":"):continueuserID, _ , _ = 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(":"):continueuserID, 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 = 0sum_x = 0sum_y = 0sum_x2 = 0sum_y2 = 0num = 0for key in rating1.keys():if key in rating2.keys():num += 1x = rating1[key]y = rating2[key]sum_xy += x * ysum_x += xsum_y += ysum_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  0else: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 = 0result = self.recommend(userID)[:self.n_items]for (item,rate) in result:if item in self.test[userID]:hit += 1precisions.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 = 30k = 15n_items =20f_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', 9.999999999999998), ('12721', 9.999999999999998), ('12672', 9.000000000000005), ('6386', 9.0), ('12918', 9.0), ('13052', 9.0), ('5085', 9.0), ('6030', 9.0), ('7928', 9.0), ('9189', 9.0), ('12293', 9.0), ('14410', 9.0), ('14550', 9.0), ('14574', 9.0), ('223', 9.0), ('12161', 9.0), ('197', 9.0), ('1191', 9.0), ('3427', 9.0), ('13087', 9.0), ('17303', 9.0), ('1110', 9.0), ('15646', 9.0), ('17330', 9.0), ('2452', 9.0), ('3624', 9.0), ('13673', 9.0), ('996', 8.999999999999998), ('5577', 8.999999999999998), ('11022', 8.999999999999998), ('13258', 8.999999999999998), ('2152', 8.000000000000004), ('4972', 8.000000000000004), ('12470', 8.000000000000004), ('6972', 8.0), ('16668', 8.0), ('3756', 8.0), ('4123', 8.0), ('5087', 8.0), ('7406', 8.0), ('10583', 8.0), ('11607', 8.0), ('16452', 8.0), ('3894', 8.0), ('16242', 8.0), ('1406', 7.999999999999999), ('1962', 7.999999999999999), ('2342', 7.999999999999999), ('2862', 7.999999999999999), ('6134', 7.999999999999999), ('6615', 7.999999999999999), ('15563', 7.999999999999999), ('3638', 7.999999999999999), ('4384', 7.999999999999999), ('9818', 7.999999999999999), ('5320', 7.999999999999999), ('6475', 7.999999999999999), ('6859', 7.999999999999999), ('15063', 7.999999999999999), ('15099', 7.999999999999999), ('15409', 7.999999999999999), ('10729', 7.999999999999998), ('13380', 7.999999999999998), ('11149', 7.000000000000005), ('6287', 7.0000000000000036), ('14712', 7.000000000000001), ('3282', 7.0), ('11677', 7.0), ('15107', 7.0), ('15788', 7.0), ('4262', 7.0), ('12056', 7.0), ('14187', 7.0), ('10421', 7.0), ('13728', 6.999999999999999), ('17149', 6.999999999999999), ('9054', 6.999999999999999), ('11314', 6.999999999999999), ('11182', 6.999999999999998), ('5814', 6.999999999999998), ('2112', 6.999999999999998), ('4996', 6.0), ('7987', 6.0), ('12155', 6.0), ('6037', 6.0), ('3860', 5.999999999999999), ('10429', 5.999999999999999), ('571', 5.999999999999998), ('6648', 5.999999999999998), ('7060', 5.0), ('14533', 5.0), ('1102', 5.0), 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('17215', 3.999999999999999), ('17762', 3.999999999999999), ('2192', 3.999999999999999), ('3347', 3.999999999999999), ('13342', 3.999999999999999), ('5071', 3.0000000000000036), ('12694', 3.0000000000000036), ('3197', 3.0), ('4745', 3.0), ('7446', 3.0), ('8782', 3.0), ('11064', 3.0), ('11837', 3.0), ('12343', 3.0), ('15339', 3.0), ('16765', 3.0), ('720', 3.0), ('1180', 3.0), ('1673', 3.0), ('2874', 3.0), ('3730', 3.0), ('4043', 3.0), ('4488', 3.0), ('5952', 3.0), ('6347', 3.0), ('7649', 3.0), ('8784', 3.0), ('9381', 3.0), ('10042', 3.0), ('10423', 3.0), ('10818', 3.0), ('13384', 3.0), ('13413', 3.0), ('13636', 3.0), ('13827', 3.0), ('13845', 3.0), ('14367', 3.0), ('14653', 3.0), ('15902', 3.0), ('16792', 3.0), ('16891', 3.0), ('2678', 3.0), ('3434', 3.0), ('3772', 3.0), ('5819', 3.0), ('7032', 3.0), ('14977', 3.0), ('5528', 3.0), ('5760', 3.0), ('8799', 3.0), ('14278', 3.0), ('2518', 3.0), ('4092', 3.0), ('5604', 3.0), ('6311', 3.0), ('7322', 3.0), ('10789', 3.0), ('15529', 3.0), ('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

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