├── .idea ├── RCM-Front.iml ├── misc.xml ├── modules.xml └── workspace.xml ├── RCMSYS.py ├── README.md ├── data ├── sc │ ├── list.csv │ ├── rank.txt │ ├── rating.csv │ └── test.txt ├── tmp.txt └── tmp │ ├── cailiao.txt │ ├── caiwuchu.txt │ ├── chengshiyutiedao.txt │ ├── chuanmei.txt │ ├── dianxin.txt │ ├── hangli.txt │ ├── jianzhu.txt │ ├── jiaoyun.txt │ ├── jingguan.txt │ ├── jixie.txt │ ├── qiche.txt │ ├── rank.txt │ ├── ruanjian.txt │ ├── test.txt │ ├── tumu.txt │ └── zhongde.txt ├── dbapi ├── __init__.py └── orm-mysql.py ├── rcmapi ├── __init__.py ├── __init__.pyc ├── rec_api.py ├── rec_api.pyc ├── recommendations.py └── recommendations.pyc ├── sql └── scrcmsys.sql ├── static ├── css │ ├── bootstrap.min.css │ └── jquery.dataTables.min.css ├── js │ ├── bootstrap.min.js │ └── jquery-2.1.3.min.js ├── lang-logo.png ├── pic │ ├── overall1.png │ ├── overall2.png │ └── overall3.png ├── tj1.png └── tj3.png └── templates ├── adduser.html ├── base.html ├── books.html ├── error.html ├── getid.html ├── index.html ├── newman.html ├── overall.html └── ranking.html /.idea/RCM-Front.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 15 | 16 | 17 | 19 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/workspace.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 109 | 110 | 111 | 112 | <form method="post">{% csrf_token %} 113 | lang 114 | container 115 | logo 116 | lang-logo 117 | recbooks 118 | path 119 | 77 120 | _path 121 | model 122 | su 123 | prin 124 | load_ran 125 | 0xa7 126 | tmp 127 | 128 | 129 | 130 | 159 | 160 | 161 | 162 | 163 | true 164 | DEFINITION_ORDER 165 | 166 | 167 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 194 | 195 | 198 | 199 | 200 | 201 | 204 | 205 | 208 | 209 | 212 | 213 | 214 | 215 | 218 | 219 | 222 | 223 | 226 | 227 | 228 | 229 | 232 | 233 | 236 | 237 | 240 | 241 | 244 | 245 | 246 | 247 | 250 | 251 | 254 | 255 | 258 | 259 | 262 | 263 | 264 | 265 | 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| 837 | 838 | 839 | 840 | 841 | 842 | 843 | 844 | 845 | 846 | 847 | 848 | 849 | 850 | 851 | 852 | 853 | 854 | 855 | 856 | 857 | 858 | 859 | 860 | 861 | 862 | 863 | 864 | 865 | 866 | 867 | 868 | 869 | 870 | 871 | 872 | 873 | 874 | 875 | 876 | 877 | 878 | 879 | 880 | 881 | 882 | 883 | 884 | 885 | 886 | 887 | 888 | 889 | 890 | 891 | 892 | 893 | 894 | 895 | 896 | 897 | 898 | 899 | 900 | 901 | 902 | 903 | 904 | 905 | 906 | 907 | 908 | 909 | -------------------------------------------------------------------------------- /RCMSYS.py: -------------------------------------------------------------------------------- 1 | #encoding:utf-8 2 | import time 3 | from flask import Flask 4 | from flask import render_template,redirect,request,url_for,session,json 5 | import pickle 6 | from rcmapi.rec_api import * 7 | import sys 8 | reload(sys) 9 | sys.setdefaultencoding('gbk') 10 | 11 | _path = sys.path 12 | 13 | app = Flask(__name__) 14 | 15 | @app.route('/') 16 | def index(): 17 | return render_template('index.html') 18 | 19 | @app.route('/overall') 20 | def overall(): 21 | return render_template('overall.html') 22 | 23 | @app.route('/get_id_page') 24 | def get_id_page(): 25 | return render_template('getid.html') 26 | 27 | @app.route('/get_id',methods =['POST']) 28 | def get_id(): 29 | try: 30 | _id = request.form['userid'] 31 | print(_id) 32 | base_user,base_item,ranklist = recbooks(_id) 33 | return render_template('books.html', id = _id, base_user = base_user, base_item = base_item, ranklist = ranklist) 34 | except Exception as e: 35 | return render_template('error.html',e = e) 36 | 37 | @app.route('/adduser',methods=['POST']) 38 | def adduser(): 39 | return render_template('adduser.html') 40 | 41 | @app.route('/get_department',methods=["POST"]) 42 | def get_department(): 43 | try: 44 | _department = request.form['department'] 45 | department_list = {1:'cailiao',2:'caiwuchu',3:'chengshiyutiedao',4:'dianxin',5:'chuanmei',6:'hangli',7:'jianzhu',8:'jiaoyun',9:'jingguan',10:'jixie',11:'qiche',12:'ruanjian',13:'tumu',14:'zhongde'} 46 | rankings = pickle.load(open(_path[0] + "/data/tmp.txt", "r")) 47 | ranklist = load_rank(_path[0] + '\\data\\sc\\') 48 | return render_template('newman.html',books=rankings['tmp\\' + department_list[int(_department)]],ranklist=ranklist) 49 | except Exception as e: 50 | return render_template('error.html',e = e) 51 | 52 | if __name__ == '__main__': 53 | app.run() 54 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## 上创项目-flask+python-基于协同过滤的图书推荐系统 2 | 3 | ### flask1.0.2 + python2.7 4 | 5 | ### 运行： ``./start.sh`` 6 | 7 | ### 前端 8 | * Adduser.html：新用户注册 9 | * Base.html：主体背景 10 | * Books.html：推荐页面 11 | * Getid.html：获取id页面 12 | * Index.html：主页面 13 | 14 | ### 后端 15 | * RCM-Front.py：路由配置页面 16 | * Rec_api：api 17 | * Recommendations.py：后台处理脚本，实现推荐算法和其他功能 18 | ![](https://i.imgur.com/Diepeh8.png) 19 | 后端主要目的是实现两种协同过滤算法，基于用户和基于物品。根据调查，我们发现学校图书馆网站已经推出了图书评分（评星级）系统，所以我们将评分制度加入进了推荐系统并且基于用户的评分实现更精准的推荐。 20 | 21 | ### index 22 | ![](https://i.imgur.com/qQzDCp0.png) 23 | 24 | ### getid 25 | ![](https://i.imgur.com/Si4F50G.png) 26 | 27 | ### book 28 | ![](https://i.imgur.com/KbTEOWP.png) 29 | -------------------------------------------------------------------------------- /data/sc/list.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tedqin/RCMSYS/9448c43b8fb0adb94c0e1ea1a449012dcdc43592/data/sc/list.csv -------------------------------------------------------------------------------- /data/sc/rank.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tedqin/RCMSYS/9448c43b8fb0adb94c0e1ea1a449012dcdc43592/data/sc/rank.txt 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User(base): 16 | __tablename__ = 'users' 17 | uid = Column(String(64),primary_key=True) 18 | udepartment = Column(String(64),nullable= False) 19 | urecord = Column(Integer,nullable= False) 20 | 21 | def __repr__(self): 22 | return self.uid 23 | 24 | class Book(base): 25 | __tablename__ = 'books' 26 | bid = Column(String(64),primary_key=True) 27 | bname = Column(String(64),nullable= False) 28 | bpopularity = Column(Integer,nullable= False) 29 | 30 | def __repr__(self): 31 | return self.bid 32 | 33 | class Record(base): 34 | __tablename__ = 'records' 35 | rid = Column(String(64),primary_key=True) 36 | ruid = Column(String(64),ForeignKey('users.uid')) 37 | rbid = Column(String(64),ForeignKey('books.bid')) 38 | rlendtime = Column(Time,nullable= False) 39 | rreturntime = Column(Time,nullable= False) 40 | 41 | def __repr__(self): 42 | return self.rid 43 | 44 | # 创建数据库连接参数和连接池大小 45 | engine = create_engine("mysql+pymysql://user:passwd@localhost/yourdb?charset=utf8",max_overflow=20) 46 | DBSession = sessionmaker(bind=engine) 47 | 48 | # 创建所有的表 49 | base.metadata.create_all(engine) -------------------------------------------------------------------------------- /rcmapi/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tedqin/RCMSYS/9448c43b8fb0adb94c0e1ea1a449012dcdc43592/rcmapi/__init__.py -------------------------------------------------------------------------------- /rcmapi/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tedqin/RCMSYS/9448c43b8fb0adb94c0e1ea1a449012dcdc43592/rcmapi/__init__.pyc -------------------------------------------------------------------------------- /rcmapi/rec_api.py: -------------------------------------------------------------------------------- 1 | #encoding:utf-8 2 | import json 3 | from recommendations import * 4 | import os 5 | import sys 6 | reload(sys) 7 | sys.setdefaultencoding('gbk') 8 | _path = os.path.dirname(os.path.abspath(__file__)) + '\\..\\data\\sc\\' 9 | 10 | def recbooks(_id): 11 | prefs = loaddata(_path) 12 | books=transformPrefs(prefs) 13 | 14 | dic = getRecommendations(prefs, _id)[0:30] 15 | base_user = dic #基于用户推荐 16 | print ("base user",base_user) 17 | 18 | itemsim = loaditem(_path) 19 | base_item = getRecommendedItems(prefs, itemsim, _id)[0:30] #基于物品推荐 20 | print ("base item", base_item) 21 | 22 | ranklist = load_rank(_path) #前30热销 23 | print ("ranklist", ranklist) 24 | 25 | return base_user,base_item,ranklist 26 | 27 | if __name__ == "__main__": 28 | recbooks('9461X77') -------------------------------------------------------------------------------- /rcmapi/rec_api.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tedqin/RCMSYS/9448c43b8fb0adb94c0e1ea1a449012dcdc43592/rcmapi/rec_api.pyc -------------------------------------------------------------------------------- /rcmapi/recommendations.py: -------------------------------------------------------------------------------- 1 | #encoding:utf-8 2 | # -*- coding:utf-8 -*- 3 | from math import sqrt 4 | from collections import Counter 5 | import sys 6 | reload(sys) 7 | sys.setdefaultencoding('gbk') 8 | 9 | _path = sys.path 10 | 11 | ##########################################################################基于用户的过滤####################################3 12 | #欧几里得距离 13 | def sim_distance(prefs,person1,preson2): 14 | si={} 15 | for item in prefs[person1]: 16 | if item in prefs[preson2]: 17 | si[item]=1 18 | if len(si)==0:return 0 19 | n=len(si) 20 | #printn 21 | sum_of_squares=sum([pow(prefs[person1][item]-prefs[preson2][item],2) for item in prefs[person1] if item in prefs[preson2]]) 22 | return 1/(1+sqrt(sum_of_squares)) 23 | 24 | #皮尔逊距离 25 | def sim_pearson(prefs,p1,p2): 26 | si={} 27 | for item in prefs[p1]: 28 | if item in prefs[p2]: 29 | si[item]=1 30 | n=len(si) 31 | if n==0: 32 | return 1 33 | 34 | #所有偏好求和 35 | sum1=sum([prefs[p1][it] for it in si]) 36 | sum2=sum([prefs[p2][it] for it in si]) 37 | 38 | #所有偏好求平方和 39 | sum1Sq=sum([pow(prefs[p1][it],2) for it in si]) 40 | sum2Sq=sum([pow(prefs[p2][it],2) for it in si]) 41 | 42 | #乘积之和 43 | pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si]) 44 | 45 | #计算皮尔逊相关系数 46 | num=pSum-(sum1*sum2/n) 47 | den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n)) 48 | 49 | if den==0: 50 | return 0 51 | r=num/den 52 | return r 53 | 54 | #对person计算出相似的有关人员和相似度评价值 55 | # 从反映偏好的字典中返回最为匹配者 56 | # 返回结果的个数和相似度函数均为可选参数 57 | def topMatches(prefs, person, n = 5, similarity = sim_pearson): 58 | scores = [(similarity(prefs, person, other), other)for other in prefs if other != person] 59 | # 参见列表推导式 60 | # 对列表排序，评价值最高的排在前面 61 | scores.sort() # 排序 62 | scores.reverse() # 反向列表中元素 63 | return scores[0:n] 64 | 65 | 66 | #对person使用pearson协同过滤得到推荐 67 | # 利用所有他人评价值的加权平均，为某人提供建议 68 | def getRecommendations(prefs,person,similarity = sim_pearson): 69 | totals = {} 70 | simSums = {} 71 | sim_list = {} 72 | i = 1 73 | for other in prefs: 74 | # 不要和自己作比较 75 | if other == person: continue 76 | sim = similarity(prefs, person, other) 77 | sim_list[other] = sim 78 | sorted(sim_list.items(), key=lambda x: x[1], reverse=True) 79 | 80 | iter = 0 81 | for other in sim_list: 82 | if iter > 30: 83 | break 84 | iter += 1 85 | # 不要和自己作比较 86 | if other ==person: continue 87 | sim = similarity(prefs, person, other) 88 | #忽略评价值为零或小于零的情况 89 | if sim <= 0: continue 90 | for item in prefs[other]: 91 | #只对自己还没看过的书籍评价 92 | if item not in prefs[person] or prefs[person][item] == 0: 93 | # 相似度 * 评价值 94 | totals.setdefault(item, 0) 95 | # setdefault() 函数: 如果键不已经存在于字典中，将会添加键并将值设为默认值。 96 | # dict.setdefault(key, default=None) 97 | # key -- 查找的键值。 98 | # default -- 键不存在时，设置的默认键值。 99 | totals[item] += prefs[other][item] * sim 100 | # 相似度之和 101 | simSums.setdefault(item, 0) 102 | simSums[item] += sim 103 | i+=1 104 | 105 | # 建立一个归一化的列表 106 | rankings = [(total / simSums[item], item) for item, total in totals.items()] # 列表推导式 107 | # 返回经过排序的列表 108 | rankings.sort() 109 | rankings.reverse() 110 | return rankings 111 | 112 | #得到与prefs商品相近的商品 113 | #这个函数就是将字典里面的人员和物品对调 114 | def transformPrefs(prefs): 115 | result={} 116 | for person in prefs: 117 | for item in prefs[person]: 118 | result.setdefault(item,{}) 119 | result[item][person]=prefs[person][item] 120 | return result 121 | 122 | 123 | ##########################################################################基于物品的过滤#################################### 124 | #得到不同物品以及与该物品相似的其他物品的矩阵 125 | def calculateSimilarItems(prefs, n = 10): 126 | # 建立字典，以给出与这些物品最为相近的所有其他物品 127 | result = {} 128 | # 以物品为中心对偏好矩阵实施倒置处理 129 | itemPrefs = transformPrefs(prefs) 130 | c = 0 131 | for item in itemPrefs: 132 | # 针对大数据集更新状态变量 133 | c += 1 134 | if c % 100 == 0:print ("%d / %d" % (c, len(itemPrefs))) 135 | # 寻找最为相近的物品 136 | scores = topMatches(itemPrefs, item, n = n, similarity = sim_distance) 137 | result[item] = scores 138 | return result # 返回一个包含物品及其最相近物品列表的字典 139 | 140 | #基于物品推荐 141 | def getRecommendedItems(prefs,itemMatch,user): 142 | userRatings=prefs[user] 143 | scores={} 144 | totalSim={} 145 | #循环遍历与当前物品相近的物品 146 | for (item,rating) in userRatings.items(): 147 | 148 | for (similarity,item2) in itemMatch[item]: 149 | #如果该用户已经对当前物品做出过评价，就忽略 150 | if item2 in userRatings:continue 151 | 152 | #评价值与相似度的加权之和 153 | scores.setdefault(item2,0) 154 | scores[item2]+=similarity*rating 155 | 156 | #全部相似度之和 157 | totalSim.setdefault(item2,0) 158 | totalSim[item2]+=similarity 159 | 160 | #将每个和值除以加权和，求平均值 161 | rankings=[(score/totalSim[item],item) for item,score in scores.items()] 162 | 163 | #排序 164 | rankings.sort() 165 | rankings.reverse() 166 | return rankings 167 | 168 | def loaddata(path): 169 | books={} 170 | for line in open(path+'list.csv'): 171 | (id,title)=line.split(',')[0:2] 172 | books[id]=title 173 | 174 | # Load data 175 | prefs={} 176 | for line in open(path+'rating.csv'): 177 | (user,bookid,rating)=line.split(',') 178 | prefs.setdefault(user,{}) 179 | prefs[user][books[bookid]]=float(rating) 180 | return prefs 181 | 182 | def loaditem(path): 183 | # 存储itemsim字典 184 | # f_item = open('itemsim.model', 'w') 185 | # f_item.write(str(itemsim)) 186 | # f_item.close() 187 | #读取itemsim字典 188 | f_item = open(path+'itemsim.model', 'r') 189 | a = f_item.read() 190 | itemsim = eval(a) 191 | f_item.close() 192 | return itemsim 193 | 194 | def ranking(path): 195 | rank_list = [] 196 | with open(path+'test.txt', 'r') as f: 197 | for line in f.readlines(): 198 | rank_list.extend(line.split()) 199 | #print(Counter(rank_list)) 200 | most_ranking = Counter(rank_list).most_common(10) 201 | 202 | books = {} 203 | for line in open(path + 'list.csv'): 204 | (id,title)=line.split(',')[0:2] 205 | books[id]=title 206 | #print(most_ranking[0][0]) 207 | #print(books[most_ranking[0][0]]) 208 | f_rank = open('book.ranking', 'w') 209 | f_rank.write(str(most_ranking)) 210 | #f_rank.write(books[most_ranking[0][0]]) 211 | f_rank.close() 212 | 213 | return most_ranking 214 | 215 | def load_rank(path): 216 | rank_list = [] 217 | with open(path+'rank.txt', 'r') as f: 218 | for line in f.readlines(): 219 | rank_list.extend(line.split('\r\n')) 220 | #print(Counter(rank_list)) 221 | return rank_list 222 | 223 | 224 | if __name__ == '__main__': 225 | pass -------------------------------------------------------------------------------- /rcmapi/recommendations.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tedqin/RCMSYS/9448c43b8fb0adb94c0e1ea1a449012dcdc43592/rcmapi/recommendations.pyc -------------------------------------------------------------------------------- /sql/scrcmsys.sql: -------------------------------------------------------------------------------- 1 | /* 2 | Navicat MySQL Data Transfer 3 | 4 | Source Server : localhost 5 | Source Server Version : 50718 6 | Source Host : localhost:3306 7 | Source Database : scrcmsys 8 | 9 | Target Server Type : MYSQL 10 | Target Server Version : 50718 11 | File Encoding : 65001 12 | 13 | Date: 2018-04-12 20:58:10 14 | */ 15 | 16 | SET FOREIGN_KEY_CHECKS=0; 17 | 18 | -- ---------------------------- 19 | -- Table structure for `books` 20 | -- ---------------------------- 21 | DROP TABLE IF EXISTS `books`; 22 | CREATE TABLE `books` ( 23 | `bid` varchar(64) NOT NULL, 24 | `bname` varchar(64) NOT NULL, 25 | `bpopularity` int(11) NOT NULL, 26 | PRIMARY KEY (`bid`) 27 | ) ENGINE=InnoDB DEFAULT CHARSET=utf8; 28 | 29 | -- ---------------------------- 30 | -- Records of books 31 | -- ---------------------------- 32 | 33 | -- ---------------------------- 34 | -- Table structure for `records` 35 | -- ---------------------------- 36 | DROP TABLE IF EXISTS `records`; 37 | CREATE TABLE `records` ( 38 | `rid` varchar(64) NOT NULL, 39 | `ruid` varchar(64) DEFAULT NULL, 40 | `rbid` varchar(64) DEFAULT NULL, 41 | `rlendtime` time NOT NULL, 42 | `rreturntime` time NOT NULL, 43 | PRIMARY KEY (`rid`), 44 | KEY `ruid` (`ruid`), 45 | KEY `rbid` (`rbid`), 46 | CONSTRAINT `records_ibfk_1` FOREIGN KEY (`ruid`) REFERENCES `users` (`uid`), 47 | CONSTRAINT `records_ibfk_2` FOREIGN KEY (`rbid`) REFERENCES `books` (`bid`) 48 | ) ENGINE=InnoDB DEFAULT CHARSET=utf8; 49 | 50 | -- ---------------------------- 51 | -- Records of records 52 | -- ---------------------------- 53 | 54 | -- ---------------------------- 55 | -- Table structure for `users` 56 | -- ---------------------------- 57 | DROP TABLE IF EXISTS `users`; 58 | CREATE TABLE `users` ( 59 | `uid` varchar(64) NOT NULL, 60 | `udepartment` varchar(64) NOT NULL, 61 | `urecord` int(11) NOT NULL, 62 | PRIMARY KEY (`uid`) 63 | ) ENGINE=InnoDB DEFAULT CHARSET=utf8; 64 | 65 | -- ---------------------------- 66 | -- Records of users 67 | -- ---------------------------- 68 | 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