├── BPR+MatrixFactorization.py ├── LICENSE ├── README.md ├── RNN+BPR.py ├── RNN+BPR2.py ├── SVD.py ├── data ├── test.txt ├── train.txt └── user_cart.json └── result ├── bpr_test_result ├── rnn_bpr_result └── svd_test_result /BPR+MatrixFactorization.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on 06/09/2017 6:05 PM 5 | 6 | @author: Tangrizzly 7 | """ 8 | # best result: 27*1000 times bootstrap sampling 9 | # train 346462.827393 10 | # test 88397.3271923 11 | 12 | import numpy as np 13 | 14 | 15 | def sigmoid(x): 16 | return 1 / (1 + np.exp(-x)) 17 | 18 | 19 | def update(w, hi, hj, learning_rate, regularization): 20 | xi = np.dot(w, hi.T) 21 | xj = np.dot(w, hj.T) 22 | xij = xi - xj 23 | w += learning_rate * (np.exp(-xij) * sigmoid(xij) * (hi - hj) + w * regularization) 24 | hi += learning_rate * (np.exp(-xij) * sigmoid(xij) * (w) + hi * regularization) 25 | hj += learning_rate * (np.exp(-xij) * sigmoid(xij) * (-w) + hj * regularization) 26 | 27 | 28 | def rse_train(): 29 | rse_train = 0 30 | for rui in train_orgi: 31 | utrain = int(rui[0]) 32 | itrain = int(rui[1]) 33 | rui_hat = np.dot(W[utrain, :], H[itrain, :].T) 34 | rse_train += np.square(rui[2] - rui_hat) + lmd * ( 35 | np.square(np.linalg.norm(W[utrain, :])) + np.square(np.linalg.norm(H[itrain, :].T))) 36 | return rse_train 37 | 38 | 39 | def rse_test(): 40 | rse_test = 0 41 | for rui in test_orgi: 42 | utest = int(rui[0]) 43 | itest = int(rui[1]) 44 | if itest>=1680: 45 | rse_test += 0 46 | else: 47 | rui_hat = np.dot(W[utest, :], H[itest, :].T) 48 | rse_test += np.square(rui[2] - rui_hat) + lmd * ( 49 | np.square(np.linalg.norm(W[utest, :])) + np.square(np.linalg.norm(H[itest, :].T))) 50 | return rse_test 51 | 52 | test_orgi = np.loadtxt("./data/test.txt") 53 | train_orgi = np.loadtxt("./data/train.txt") 54 | 55 | us = np.unique(train_orgi[:, 0]).shape[0]+1 56 | it = int(np.max(train_orgi[:, 1])+1) 57 | avg = np.average(train_orgi[:, 2]) 58 | 59 | # initialization 60 | np.random.seed(7) 61 | f = 20 62 | gm = 0.005 63 | lmd = 0.02 64 | W = np.random.rand(us, f) 65 | H = np.random.rand(it, f) 66 | for b in range(0, 50): 67 | for a in range(0, 1000): 68 | u = np.random.choice(train_orgi[:, 0], 1)[0] 69 | [i, j] = np.random.choice(train_orgi[:, 1], 2) 70 | update(W[int(u), :], H[int(i), :], H[int(j), :], gm, lmd) 71 | print rse_train() 72 | print rse_test() 73 | print b 74 | 75 | X = np.dot(W, H.T) 76 | for rui in test_orgi: 77 | a = int(rui[0]) 78 | b = int(rui[1]) 79 | if b >= 1680: 80 | rui_hat=avg 81 | else: 82 | rui_hat = X[a, b] 83 | rui[3] = rui_hat 84 | 85 | np.savetxt('bpr_result', test_orgi, fmt='%.2f') -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Tangrizzly 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 3RecommendationSystemAlgorithms 2 | 3 | ### SVD & BPR+MatrixFactorization 4 | using a movie rating dataset 5 | 6 | ### RNN+BPR+BPTT 7 | using taobao marketing dataset 8 | 9 | ### reference: 10 | 1. Yehuda Koren and Robert Bell: Advances in Collaborative Filtering 11 | 2. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme: BPR: Bayesian Personalized Ranking from Implicit Feedback 12 | 3. Liu, Wu, Wang: Context-aware Sequential Recommendation 13 | -------------------------------------------------------------------------------- /RNN+BPR.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on 06/09/2017 9:54 PM 5 | 6 | @author: Tangrizzly 7 | """ 8 | import re 9 | import numpy as np 10 | from datetime import datetime 11 | import sys 12 | import pandas as pd 13 | 14 | 15 | def sigmoid(x): 16 | return 1 / (1 + np.exp(-x)) 17 | 18 | class RnnBpr: 19 | def __init__(self, d, x, lmd): 20 | self.d = d 21 | self.x = x 22 | self.lmd = lmd 23 | self.v = np.unique(x) 24 | self.R = np.random.rand(np.max(x)+1, d) 25 | self.M = np.random.rand(d, d) 26 | self.W = np.random.rand(d, d) 27 | 28 | def forward_propagation(self): 29 | K = len(self.x) 30 | h = np.zeros((K + 1, self.d)) 31 | h[-1] = np.zeros(self.d) 32 | y = np.zeros(K).reshape(K, 1) 33 | yi = np.zeros(K).reshape(K, 1) 34 | recall = 0 35 | for k in np.arange(K): 36 | h[k] = sigmoid(np.dot(self.R[self.x[k]].reshape(1, self.d), self.M) + np.dot(h[k - 1].reshape(1, self.d), self.W)) 37 | # y[k] = np.dot(np.dot(h[k], self.W), np.dot(self.R[x[k]], self.M).T) 38 | y[k] = np.dot(h[k-1].reshape(1, self.d), self.R[self.x[k]].reshape(self.d, 1)) 39 | i = np.random.choice(self.v, 1)[0] 40 | # yi[k] = np.dot(np.dot(h[k], self.W), np.dot(r[i], self.M).T) 41 | yi[k] = np.dot(h[k-1].reshape(1, self.d), self.R[i].reshape(self.d, 1)) 42 | y_hat = np.argsort(np.dot(h[k - 1], self.R.T))[-10:][::-1] 43 | if self.x[k] in y_hat: 44 | recall += 1 45 | recall_rate = recall/float(len(self.x)) 46 | return h, y, yi, recall_rate 47 | 48 | def calculate_loss(self, lmd): 49 | h, y, yi, recall_rate = self.forward_propagation() 50 | l = np.sum(np.log(1 + np.exp(-y + yi))) 51 | l += lmd / 2 * (np.square(np.linalg.norm(self.R)) 52 | + np.square(np.linalg.norm(self.W)) 53 | + np.square(np.linalg.norm(self.M)) 54 | ) 55 | return l, recall_rate 56 | 57 | def bptt(self, learning_rate): 58 | K = len(self.x) 59 | h, y, yi, recall_rate = self.forward_propagation() 60 | j = np.random.choice(self.v, 1)[0] 61 | dLdh = self.R[self.x[K-1]] - self.R[j] 62 | dLdR = np.zeros(self.R.shape) 63 | for t in np.arange(K-1)[::-1]: 64 | j = np.random.choice(self.v, 1)[0] 65 | Xij = np.dot(h[t], self.R[self.x[t+1]].T) - np.dot(h[t], self.R[j].T) 66 | dLdR[self.x[t+1]] = h[t] 67 | dLdR[j] = -h[t] 68 | df = np.multiply(h[t], (1 - h[t])).reshape(1, self.d) 69 | dLdM = np.dot(self.R[self.x[t]].reshape(self.d, 1), dLdh * df) 70 | dLdW = np.dot(h[t-1].reshape(self.d, 1), dLdh * df) 71 | dLdh = np.dot(dLdh * df, self.W.T) 72 | self.M += learning_rate * (np.exp(-Xij)*sigmoid(Xij)*dLdM-self.lmd*self.M) 73 | self.W += learning_rate * (np.exp(-Xij)*sigmoid(Xij)*dLdW-self.lmd*self.W) 74 | self.R[self.x[t+1]] += learning_rate * (np.exp(-Xij)*sigmoid(Xij)*dLdR[self.x[t+1]]-self.lmd*self.R[self.x[t+1]]) 75 | self.R[j] += learning_rate * (np.exp(-Xij)*sigmoid(Xij)*dLdR[j]-self.lmd*self.R[j]) 76 | 77 | 78 | def train(model, learning_rate=0.002, nepoch=100, evaluate_loss_after=5): 79 | # We keep track of the losses so we can plot them later 80 | losses = [] 81 | num_examples_seen = 0 82 | max_recall_rate = 0.0 83 | for epoch in range(nepoch): 84 | # Optionally evaluate the loss 85 | if epoch % evaluate_loss_after == 0: 86 | loss, recall_rate = model.calculate_loss(model.lmd) 87 | max_recall_rate = np.max([max_recall_rate, recall_rate]) 88 | losses.append((num_examples_seen, loss)) 89 | time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') 90 | print "%s: Loss after num_examples_seen=%d : %f" % (time, num_examples_seen, loss) 91 | print "Max recall rate: %f" % max_recall_rate 92 | # Adjust the learning rate if loss increases 93 | # if len(losses) > 1 and losses[-1][1] > losses[-2][1]: 94 | # learning_rate = learning_rate * 0.5 95 | # print "Setting learning rate to %f" % learning_rate 96 | sys.stdout.flush() 97 | model.bptt(learning_rate) 98 | num_examples_seen += 1 99 | return max_recall_rate 100 | 101 | 102 | with open('./data/user_cart.json', 'r') as f: 103 | data = f.readlines() 104 | train_orig = [] 105 | temp = [] 106 | for line in data: 107 | line = re.sub('[\[|\]|\s++]', '', line) 108 | odom = line.split(',') 109 | if len(odom) < 10: 110 | continue 111 | numbers_float = map(float, odom) 112 | numbers = map(int, numbers_float) 113 | train_orig.append(numbers) 114 | temp.extend(numbers) 115 | 116 | # remove items appearing less than 3 times 117 | trainSet = [] 118 | testSet = [] 119 | temp_df = pd.DataFrame(temp) 120 | lessThan3 = temp_df[0].value_counts()[temp_df[0].value_counts()<3].index[:] 121 | for line in train_orig: 122 | cleaned = [x for x in line if x not in lessThan3] 123 | if len(cleaned) >= 10: 124 | trainSet.append(cleaned) 125 | # split train set and test set 126 | # if len(cleaned) >= 10: 127 | # trainSet.append(cleaned[0: int(len(cleaned) * 0.8)-1]) 128 | # testSet.append(cleaned[int(len(cleaned) * 0.8): -1]) 129 | 130 | np.random.seed(1) 131 | recall = [] 132 | for rui in np.arange(len(trainSet)): 133 | model = RnnBpr(10, trainSet[rui], 0.01) 134 | recall.append(train(model, evaluate_loss_after=1)) 135 | print rui 136 | np.savetxt('rnn_bpr_result', np.asarray(recall), fmt='%f') -------------------------------------------------------------------------------- /RNN+BPR2.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on 06/09/2017 9:54 PM 5 | Modified on 25/11/2017 8:37 AM 6 | @author: Tangrizzly 7 | """ 8 | import numpy as np 9 | from datetime import datetime 10 | from numpy.random import rand 11 | # from numpy.random import uniform 12 | from collections import Counter 13 | 14 | 15 | def sigmoid(x): 16 | return 1 / (1 + np.exp(-x)) 17 | 18 | 19 | class RnnBpr: 20 | def __init__(self, d, dataset, v, maxNo, lmd): 21 | self.d = d # 隐层维度 22 | self.lmd = lmd # lambda 23 | self.dataset = dataset 24 | self.v = v # dataset里的items 25 | self.R = rand(maxNo + 1, d) # 所有item的向量表示 26 | self.M = rand(self.d, self.d) # uniform(-0.5, 0.5, (d, d))指定范围 27 | self.W = rand(self.d, self.d) 28 | 29 | def forward_propagation(self, x): 30 | K = len(x) 31 | h = np.zeros((K + 1, self.d)) 32 | h[-1] = np.zeros(self.d) 33 | y = np.zeros(K).reshape((K, 1)) # 注意reshape函数的格式 34 | yi = np.zeros(K).reshape((K, 1)) 35 | for k in np.arange(K): 36 | h[k] = sigmoid(np.dot(self.M, self.R[x[k]]) + np.dot(self.W, h[k - 1])) 37 | # 之前写的是点乘得到的结果 38 | y[k] = np.sum(h[k - 1] * self.R[x[k]]) # 对正样本的偏好值, 应该是pair-wise乘法之后加和 39 | i = int(np.random.choice(self.v, 1)[0]) # 负样本 40 | yi[k] = np.sum(h[k - 1] * self.R[i]) # 对负样本的偏好值 41 | # y_hat = np.argsort(np.dot(h[K-1], self.R.T))[-10:][::-1] # 将所有的评分进行排序之后取前十,计算量过大 42 | # 先得到前十之后进行排序 43 | scores = np.dot(h[K - 1], self.R.T) # user对所有items的评分 44 | max_10 = np.argpartition(scores, -10)[-10:] # 复杂度O(n),获得评分前十个的index 45 | y_hat = max_10[np.argsort(scores[max_10])][::-1] # 复杂度O(nlogn),得到前十个得分最高的items_ids推荐给用户 46 | return h, y, yi, y_hat # 得到前十个得分最大的对应的item_ids,并且最左边的是得分最大的 47 | 48 | def calculate_loss(self, lmd, x): 49 | h, y, yi, y_hat = self.forward_propagation(x) 50 | # l = np.sum(np.log(1 + np.exp(-y + yi))) # ??? 51 | l = np.sum(np.log(1 + np.exp(y - yi))) # 对正样本偏好减对负样本偏好 52 | l += lmd / 2 * (np.square(np.linalg.norm(self.R)) 53 | + np.square(np.linalg.norm(self.W)) 54 | + np.square(np.linalg.norm(self.M)) 55 | ) 56 | return l, y_hat 57 | 58 | def bptt(self, x, learning_rate): 59 | # 在此写的是BP,而非BPTT(汇总损失，仅更新一大步) 60 | K = len(x) 61 | h, y, yi, y_hat = self.forward_propagation(x) 62 | j = int(np.random.choice(self.v, 1)[0]) 63 | dLdh = self.R[x[K - 1]] - self.R[j] 64 | dLdR = np.zeros(self.R.shape) 65 | for t in np.arange(K - 1)[::-1]: 66 | j = int(np.random.choice(self.v, 1)[0]) 67 | Xij = np.dot(h[t], self.R[x[t + 1]].T) - np.dot(h[t], self.R[j].T) 68 | dLdR[x[t + 1]] = h[t] 69 | dLdR[j] = -h[t] 70 | df = np.multiply(h[t], (1 - h[t])).reshape(1, self.d) 71 | dLdM = np.dot(dLdh * df, self.R[x[t]].reshape((self.d, 1))) 72 | dLdW = np.dot(dLdh * df, h[t - 1].reshape((self.d, 1))) 73 | dLdh = np.dot(dLdh * df, self.W.T) 74 | self.M += learning_rate * (np.exp(-Xij) * sigmoid(Xij) * dLdM - self.lmd * self.M) 75 | self.W += learning_rate * (np.exp(-Xij) * sigmoid(Xij) * dLdW - self.lmd * self.W) 76 | self.R[x[t + 1]] += learning_rate * ( 77 | np.exp(-Xij) * sigmoid(Xij) * dLdR[x[t + 1]] - self.lmd * self.R[x[t + 1]]) 78 | self.R[j] += learning_rate * (np.exp(-Xij) * sigmoid(Xij) * dLdR[j] - self.lmd * self.R[j]) 79 | 80 | 81 | # def train(model, learning_rate=0.002): 82 | # """ 83 | # :param model: RnnBpr 84 | # :param learning_rate: 一般采用0.1/0.01这种10倍间隔的，中间值可选用0.05这种。 85 | # :return: recall_rate 86 | # """ 87 | # losses = [] 88 | # recall = 0 89 | # for i in range(0, len(model.x)): 90 | # xi = model.x[0: i + 1] # shape=(i+1, d) 91 | # loss, y_hat = model.calculate_loss(model.lmd, xi) 92 | # if i < len(model.x) - 1: 93 | # if model.x[i + 1] in y_hat: 94 | # recall += 1 95 | # losses.append(loss) 96 | # time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') 97 | # print "%s: Loss %f, recall %d"% (time, loss, recall) 98 | # model.bptt(xi, learning_rate) 99 | # recall_rate = recall / float(len(model.x) - 1) 100 | # return recall_rate 101 | 102 | def train(model, learning_rate=0.01): 103 | """ 104 | :param model: RnnBpr 105 | :param learning_rate: 一般采用0.1/0.01这种10倍间隔的，中间值可选用0.05这种。 106 | :return: recall_rate 107 | """ 108 | example = 0 109 | time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') 110 | print "%s: Loss after example=%d : %f" % (time, example, 0) 111 | for x in model.dataset: 112 | example += 1 113 | recall = 0 114 | for i in range(0, len(x)): 115 | xi = x[0: i + 1] # shape=(i+1, d) 116 | loss, y_hat = model.calculate_loss(model.lmd, xi) 117 | if i < len(x) - 1: 118 | if x[i + 1] in y_hat: 119 | recall += 1 120 | # time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') 121 | # print "%s: Loss %f, recall %d"% (time, loss, recall) 122 | model.bptt(xi, learning_rate) 123 | recall_rate = recall / float(len(x) - 1) 124 | time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') 125 | print "%s: Loss after example=%d : %f" % (time, example, loss) 126 | print "Recall rate: %f" % recall_rate 127 | 128 | 129 | # 读取用户序列历史, n_user=737 130 | with open('./data/user_cart.json', 'r') as f: 131 | data = f.readlines() 132 | train_orig = [] # 嵌套列表,每个用户的序列=[int1, int2, ...] 133 | temp = [] 134 | for line in data: 135 | # line = re.sub('[\[|\]|\s++]', '', line) 136 | # odom = line.split(',') 137 | odem = line[1:][:-2].replace(' ', '').split(',') 138 | if len(odem) < 10: 139 | continue 140 | # numbers_float = map(float, odom) 141 | # numbers = map(int, numbers_float) 142 | numbers = [int(float(i)) for i in odem] 143 | train_orig.append(numbers) 144 | temp.extend(numbers) 145 | 146 | # 处理后,n_user=706 147 | # remove items appearing less than 3 times 148 | maxNo = 0 149 | v = [] 150 | trainSet = [] # 嵌套列表，每个用户的序列=[int1, int2, ...] 151 | # temp_df = pd.DataFrame(temp) 152 | # lessThan3 = temp_df[0].value_counts()[temp_df[0].value_counts()<3].index[:] 153 | items_count = dict(Counter(temp)) 154 | lessThan3 = [item for item, count in items_count.items() if count < 3] 155 | for line in train_orig: 156 | cleaned = [x for x in line if x not in lessThan3] 157 | maxNo = np.max([maxNo, np.max(cleaned)]) 158 | v = np.append(v, np.unique(cleaned)) 159 | if len(cleaned) >= 10: 160 | trainSet.append(cleaned) 161 | 162 | np.random.seed(1) 163 | recall = [] 164 | # model可以只建1次，一次性初始化所有的items表达 165 | model = RnnBpr(10, trainSet, v, maxNo, 0.01) 166 | train(model) 167 | # np.savetxt('rnn_bpr_result', np.asarray(recall), fmt='%f') 168 | -------------------------------------------------------------------------------- /SVD.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on 06/09/2017 9:50 AM 5 | 6 | @author: Tangrizzly 7 | """ 8 | # best result: 9 | # rse_train = 57372.4818287 10 | # rse_test = 21230.4729627 11 | # more loops will lead to overfitting 12 | 13 | import numpy as np 14 | 15 | test_orgi = np.loadtxt("./data/test.txt") 16 | train_orgi = np.loadtxt("./data/train.txt") 17 | 18 | u = np.unique(train_orgi[:, 0]).shape[0] + 1 19 | # i = np.unique(train_orgi[:, 0]).shape[0] 20 | i = int(np.max(train_orgi[:, 1])) + 1 21 | f = 20 22 | gm = 0.005 23 | lmd = 0.02 24 | avg = np.average(train_orgi[:, 2]) 25 | 26 | # initialization 27 | qi = np.random.rand(f, i) 28 | pu = np.random.rand(f, u) 29 | bi = np.zeros([i, 1]) 30 | bu = np.zeros([1, u]) 31 | # r_hat = avg + bi + bu + np.dot(qi.T, pu) 32 | 33 | for i in range(0, 19): 34 | print i 35 | for rui in train_orgi: 36 | a = int(rui[0]) 37 | b = int(rui[1]) 38 | rui_hat = avg + bi[b][0] + bu[0][a] + np.dot(qi[:, b].T, pu[:, a]) 39 | eui = rui[2] - rui_hat 40 | bu[0][a] += gm * (eui - lmd * bu[0][a]) 41 | bi[b][0] += gm * (eui - lmd * bi[b][0]) 42 | qi[:, b] += gm * (eui * pu[:, a] - lmd * qi[:, b]) 43 | pu[:, a] += gm * (eui * qi[:, b] - lmd * pu[:, a]) 44 | rse_train = 0 45 | for rui in train_orgi: 46 | a = int(rui[0]) 47 | b = int(rui[1]) 48 | rui_hat = avg + bi[b][0] + bu[0][a] + np.dot(qi[:, b].T, pu[:, a]) 49 | rse_train += np.square(rui[2]-rui_hat) + lmd*(np.square(bi[b][0])+np.square(bu[0][a])+np.square(np.linalg.norm(qi[:, b]))+np.square(np.linalg.norm(pu[:, a]))) 50 | print rse_train 51 | rse_test = 0 52 | for rui in test_orgi: 53 | a = int(rui[0]) 54 | b = int(rui[1]) 55 | if b >= 1680: 56 | rui_hat = avg 57 | rse_test += 0 58 | else: 59 | rui_hat = avg + bi[b][0] + bu[0][a] + np.dot(qi[:, b].T, pu[:, a]) 60 | rse_test += np.square(rui[2]-rui_hat) + lmd*(np.square(bi[b][0])+np.square(np.linalg.norm(qi[:, b]))+np.square(np.linalg.norm(pu[:, a]))) 61 | print rse_test 62 | 63 | for rui in test_orgi: 64 | a = int(rui[0]) 65 | b = int(rui[1]) 66 | if b>=1680: 67 | rui_hat=avg 68 | else: 69 | rui_hat = avg + bi[b][0] + bu[0][a] + np.dot(qi[:, b].T, pu[:, a]) 70 | rui[3] = rui_hat 71 | print rui 72 | 73 | np.savetxt('result', test_orgi, fmt='%.2f') 74 | 75 | 76 | 77 | -------------------------------------------------------------------------------- /result/rnn_bpr_result: -------------------------------------------------------------------------------- 1 | 0.586667 2 | 0.255937 3 | 0.048780 4 | 0.173913 5 | 0.206128 6 | 0.041667 7 | 0.000000 8 | 0.000000 9 | 0.000000 10 | 0.381818 11 | 0.000000 12 | 0.475728 13 | 0.646789 14 | 0.000000 15 | 0.000000 16 | 0.000000 17 | 0.000000 18 | 0.236220 19 | 0.000000 20 | 0.042553 21 | 0.000000 22 | 0.000000 23 | 0.000000 24 | 0.503788 25 | 0.180180 26 | 0.224852 27 | 0.000000 28 | 0.000000 29 | 0.000000 30 | 0.000000 31 | 0.204301 32 | 0.564267 33 | 0.000000 34 | 0.000000 35 | 0.427984 36 | 0.228070 37 | 0.076923 38 | 0.033708 39 | 0.359873 40 | 0.000000 41 | 0.000000 42 | 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