推薦系統項目實戰

強烈推薦按這本書哦，資料很全，也很有邏輯
新的一年，學習新的知識，這里學習了這本書，計劃兩周學完

數據集 鏈接：https://pan.baidu.com/s/1MVsdKM2q6cq-mL_I5DOt7A
提取碼：0tqo
上面鏈接失效了
但是我找不到之前的了
所以這里附上所有的資料，大家自行查找！

鏈接：https://pan.baidu.com/s/1pSHeDZQpqSkVmq3xg1OrHw
提取碼：1234

代碼

# -*- coding:utf-8 -*-"""Author: ThinkgamerDesc:代碼2-1 實例1：搭建你的第一個推薦系統-電影推薦系統從中隨機選擇1000個與用戶進行計算 """ import os import json import random import mathclass FirstRec:"""初始化函數filePath: 原始文件路徑seed：產生隨機數的種子k：選取的近鄰用戶個數nitems：為每個用戶推薦的電影數"""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()# 獲取所有用戶并隨機選取1000個def __select_1000_users(self):print("隨機選取1000個用戶！")if os.path.exists("data/train.json") and os.path.exists("data/test.json"):return list()else:users = set()# 獲取所有用戶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)# 隨機選取1000個users_1000 = random.sample(list(users),1000)print(users_1000)return users_1000# 加載數據，并拆分為訓練集和測試集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("從文件中加載訓練集和測試集")train = json.load(open("data/train.json"))test = json.load(open("data/test.json"))print("從文件中加載數據完成")else:# 設置產生隨機數的種子，保證每次實驗產生的隨機結果一致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(",")# 判斷用戶是否在所選擇的1000個用戶中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("加載數據到 data/train.json 和 data/test.json")json.dump(train,open("data/train.json","w"))json.dump(test,open("data/test.json","w"))print("加載數據完成")return train,test"""計算皮爾遜相關系數rating1：用戶1的評分記錄，形式如{"movieid1":rate1,"movieid2":rate2,...}rating2：用戶1的評分記錄，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# 皮爾遜相關系數分母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"""用戶userID進行電影推薦userID：用戶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"""推薦系統效果評估函數num: 隨機抽取 num 個用戶計算準確率"""def evaluate(self,num=30):print("開始計算準確率")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函數，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)# 計算用戶 195100 和 1547579的皮爾遜相關系數r = f_rec.pearson(f_rec.train["195100"],f_rec.train["1547579"])print("195100 和 1547579的皮爾遜相關系數為：{}".format(r))# 為用戶195100進行電影推薦result = f_rec.recommend("195100")print("為用戶ID為：195100的用戶推薦的電影為：{}".format(result))print("算法的推薦準確率為: {}".format(f_rec.evaluate()))

結果

隨機選取1000個用戶！ 從文件中加載訓練集和測試集 從文件中加載數據完成 195100 和 1547579的皮爾遜相關系數為：0.1194695382178992 為用戶ID為：195100的用戶推薦的電影為：[('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), 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('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)] 開始計算準確率 算法的推薦準確率為: 0.005000000000000001

總結
只是抽取的1000個訓練，結果并不是很理想，全部訓練集基數大，估計可行
后期有時間放上GPU結果

總結

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