diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 254b485d..e5e86293 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
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         <option value="$PROJECT_DIR$/.gitignore" />
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+        <option value="$PROJECT_DIR$/recommendation/Basic-DMF-Model/data/main.py" />
+        <option value="$PROJECT_DIR$/recommendation/Basic-DMF-Model/README.md" />
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               <item name="Basic-DIEN-Demo" type="462c0819:PsiDirectoryNode" />
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@@ -894,5 +885,49 @@
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+    <entry file="file://$PROJECT_DIR$/recommendation/Basic-DMF-Model/main.py">
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+    <entry file="file://$PROJECT_DIR$/recommendation/Basic-DMF-Model/dataset.py">
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diff --git a/recommendation/Basic-DMF-Model/README.md b/recommendation/Basic-DMF-Model/README.md
new file mode 100755
index 00000000..fc823683
--- /dev/null
+++ b/recommendation/Basic-DMF-Model/README.md
@@ -0,0 +1,6 @@
+# Deep Matrix Factorization Model
+An Implementation of "Deep Matrix Factorization Models for Recommender Systems"
+
+See paper: http://www.ijcai.org/proceedings/2017/0447.pdf
+
+Data:movieslen-1m
diff --git a/recommendation/Basic-DMF-Model/dataset.py b/recommendation/Basic-DMF-Model/dataset.py
new file mode 100644
index 00000000..db7f01c2
--- /dev/null
+++ b/recommendation/Basic-DMF-Model/dataset.py
@@ -0,0 +1,120 @@
+# -*- Encoding:UTF-8 -*-
+
+import numpy as np
+import sys
+
+
+class DataSet(object):
+    def __init__(self, fileName):
+        self.data, self.shape = self.getData(fileName)
+        self.train, self.test = self.getTrainTest()
+        self.trainDict = self.getTrainDict()
+
+    def getData(self, fileName):
+        if fileName == 'ml-1m':
+            print("Loading ml-1m data set...")
+            data = []
+            filePath = 'data/ratings.dat'
+            u = 0
+            i = 0
+            maxr = 0.0
+            with open(filePath, 'r') as f:
+                for line in f:
+                    if line:
+                        lines = line[:-1].split("::")
+                        user = int(lines[0])
+                        movie = int(lines[1])
+                        score = float(lines[2])
+                        time = int(lines[3])
+                        data.append((user, movie, score, time))
+                        if user > u:
+                            u = user
+                        if movie > i:
+                            i = movie
+                        if score > maxr:
+                            maxr = score
+            self.maxRate = maxr
+            print("Loading Success!\n"
+                  "Data Info:\n"
+                  "\tUser Num: {}\n"
+                  "\tItem Num: {}\n"
+                  "\tData Size: {}".format(u, i, len(data)))
+            return data, [u, i]
+        else:
+            print("Current data set is not support!")
+            sys.exit()
+
+    def getTrainTest(self):
+        data = self.data
+        data = sorted(data, key=lambda x: (x[0], x[3])) # 按照用户、评论时间排序
+        train = []
+        test = []
+        for i in range(len(data)-1): # 每个用户的最后一条评论数据作为测试集
+            user = data[i][0]-1
+            item = data[i][1]-1
+            rate = data[i][2]
+            if data[i][0] != data[i+1][0]:
+                test.append((user, item, rate))
+            else:
+                train.append((user, item, rate))
+
+        test.append((data[-1][0]-1, data[-1][1]-1, data[-1][2]))
+        return train, test
+
+    def getTrainDict(self):
+        dataDict = {}
+        for i in self.train:
+            dataDict[(i[0], i[1])] = i[2]
+        return dataDict
+
+    def getEmbedding(self):
+        """
+        这里的Embedding即评分矩阵
+        """
+        train_matrix = np.zeros([self.shape[0], self.shape[1]], dtype=np.float32)
+        for i in self.train:
+            user = i[0]
+            movie = i[1]
+            rating = i[2]
+            train_matrix[user][movie] = rating
+        return np.array(train_matrix)
+
+    def getInstances(self, data, negNum):
+        user = []
+        item = []
+        rate = []
+        for i in data:
+            user.append(i[0])
+            item.append(i[1])
+            rate.append(i[2])
+            for t in range(negNum):
+                j = np.random.randint(self.shape[1])
+                while (i[0], j) in self.trainDict:
+                    j = np.random.randint(self.shape[1])
+                user.append(i[0])
+                item.append(j)
+                rate.append(0.0)
+        return np.array(user), np.array(item), np.array(rate)
+
+    def getTestNeg(self, testData, negNum):
+        user = []
+        item = []
+        for s in testData:
+            tmp_user = []
+            tmp_item = []
+            u = s[0]
+            i = s[1]
+            tmp_user.append(u)
+            tmp_item.append(i)
+            neglist = set()
+            neglist.add(i)
+            for t in range(negNum):
+                j = np.random.randint(self.shape[1])
+                while (u, j) in self.trainDict or j in neglist:
+                    j = np.random.randint(self.shape[1])
+                neglist.add(j)
+                tmp_user.append(u)
+                tmp_item.append(j)
+            user.append(tmp_user)
+            item.append(tmp_item)
+        return [np.array(user), np.array(item)]
diff --git a/recommendation/Basic-DMF-Model/main.py b/recommendation/Basic-DMF-Model/main.py
new file mode 100644
index 00000000..e942a7d2
--- /dev/null
+++ b/recommendation/Basic-DMF-Model/main.py
@@ -0,0 +1,236 @@
+
+
+import tensorflow as tf
+import numpy as np
+import argparse
+from dataset import DataSet
+import sys
+import os
+import heapq
+import math
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Options")
+
+    parser.add_argument('-dataName', action='store', dest='dataName', default='ml-1m')
+    parser.add_argument('-negNum', action='store', dest='negNum', default=7, type=int)
+    parser.add_argument('-userLayer', action='store', dest='userLayer', default=[512, 64])
+    parser.add_argument('-itemLayer', action='store', dest='itemLayer', default=[1024, 64])
+    # parser.add_argument('-reg', action='store', dest='reg', default=1e-3)
+    parser.add_argument('-lr', action='store', dest='lr', default=0.0001)
+    parser.add_argument('-maxEpochs', action='store', dest='maxEpochs', default=50, type=int)
+    parser.add_argument('-batchSize', action='store', dest='batchSize', default=256, type=int)
+    parser.add_argument('-earlyStop', action='store', dest='earlyStop', default=5)
+    parser.add_argument('-checkPoint', action='store', dest='checkPoint', default='./checkPoint/')
+    parser.add_argument('-topK', action='store', dest='topK', default=10)
+
+    args = parser.parse_args()
+
+    classifier = Model(args)
+
+    classifier.run()
+
+
+class Model:
+    def __init__(self, args):
+        self.dataName = args.dataName
+        self.dataSet = DataSet(self.dataName)
+        self.shape = self.dataSet.shape
+        self.maxRate = self.dataSet.maxRate
+
+        self.train = self.dataSet.train
+        self.test = self.dataSet.test
+
+        self.negNum = args.negNum
+        self.testNeg = self.dataSet.getTestNeg(self.test, 99)
+        self.add_embedding_matrix()
+
+        self.add_placeholders()
+
+        self.userLayer = args.userLayer
+        self.itemLayer = args.itemLayer
+        self.add_model()
+
+        self.add_loss()
+
+        self.lr = args.lr
+        self.add_train_step()
+
+        self.checkPoint = args.checkPoint
+        self.init_sess()
+
+        self.maxEpochs = args.maxEpochs
+        self.batchSize = args.batchSize
+
+        self.topK = args.topK
+        self.earlyStop = args.earlyStop
+
+
+    def add_placeholders(self):
+        self.user = tf.placeholder(tf.int32)
+        self.item = tf.placeholder(tf.int32)
+        self.rate = tf.placeholder(tf.float32)
+        self.drop = tf.placeholder(tf.float32)
+
+    def add_embedding_matrix(self):
+        self.user_item_embedding = tf.convert_to_tensor(self.dataSet.getEmbedding())
+        self.item_user_embedding = tf.transpose(self.user_item_embedding)
+
+    def add_model(self):
+        user_input = tf.nn.embedding_lookup(self.user_item_embedding, self.user)
+        item_input = tf.nn.embedding_lookup(self.item_user_embedding, self.item)
+
+        def init_variable(shape, name):
+            return tf.Variable(tf.truncated_normal(shape=shape, dtype=tf.float32, stddev=0.01), name=name)
+
+        with tf.name_scope("User_Layer"):
+            user_W1 = init_variable([self.shape[1], self.userLayer[0]], "user_W1")
+            user_out = tf.matmul(user_input, user_W1)
+            for i in range(0, len(self.userLayer)-1):
+                W = init_variable([self.userLayer[i], self.userLayer[i+1]], "user_W"+str(i+2))
+                b = init_variable([self.userLayer[i+1]], "user_b"+str(i+2))
+                user_out = tf.nn.relu(tf.add(tf.matmul(user_out, W), b))
+
+        with tf.name_scope("Item_Layer"):
+            item_W1 = init_variable([self.shape[0], self.itemLayer[0]], "item_W1")
+            item_out = tf.matmul(item_input, item_W1)
+            for i in range(0, len(self.itemLayer)-1):
+                W = init_variable([self.itemLayer[i], self.itemLayer[i+1]], "item_W"+str(i+2))
+                b = init_variable([self.itemLayer[i+1]], "item_b"+str(i+2))
+                item_out = tf.nn.relu(tf.add(tf.matmul(item_out, W), b))
+
+        norm_user_output = tf.sqrt(tf.reduce_sum(tf.square(user_out), axis=1))
+        norm_item_output = tf.sqrt(tf.reduce_sum(tf.square(item_out), axis=1))
+        self.y_ = tf.reduce_sum(tf.multiply(user_out, item_out), axis=1, keep_dims=False) / (norm_item_output* norm_user_output)
+        self.y_ = tf.maximum(1e-6, self.y_)
+
+    def add_loss(self):
+        regRate = self.rate / self.maxRate
+        losses = regRate * tf.log(self.y_) + (1 - regRate) * tf.log(1 - self.y_)
+        loss = -tf.reduce_sum(losses)
+        # regLoss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
+        # self.loss = loss + self.reg * regLoss
+        self.loss = loss
+
+    def add_train_step(self):
+        '''
+        global_step = tf.Variable(0, name='global_step', trainable=False)
+        self.lr = tf.train.exponential_decay(self.lr, global_step,
+                                             self.decay_steps, self.decay_rate, staircase=True)
+        '''
+        optimizer = tf.train.AdamOptimizer(self.lr)
+        self.train_step = optimizer.minimize(self.loss)
+
+    def init_sess(self):
+        self.config = tf.ConfigProto()
+        self.config.gpu_options.allow_growth = True
+        self.config.allow_soft_placement = True
+        self.sess = tf.Session(config=self.config)
+        self.sess.run(tf.global_variables_initializer())
+
+        self.saver = tf.train.Saver()
+        if os.path.exists(self.checkPoint):
+            [os.remove(f) for f in os.listdir(self.checkPoint)]
+        else:
+            os.mkdir(self.checkPoint)
+
+    def run(self):
+        best_hr = -1
+        best_NDCG = -1
+        best_epoch = -1
+        print("Start Training!")
+        for epoch in range(self.maxEpochs):
+            print("="*20+"Epoch ", epoch, "="*20)
+            self.run_epoch(self.sess)
+            print('='*50)
+            print("Start Evaluation!")
+            hr, NDCG = self.evaluate(self.sess, self.topK)
+            print("Epoch ", epoch, "HR: {}, NDCG: {}".format(hr, NDCG))
+            if hr > best_hr or NDCG > best_NDCG:
+                best_hr = hr
+                best_NDCG = NDCG
+                best_epoch = epoch
+                self.saver.save(self.sess, self.checkPoint)
+            if epoch - best_epoch > self.earlyStop:
+                print("Normal Early stop!")
+                break
+            print("="*20+"Epoch ", epoch, "End"+"="*20)
+        print("Best hr: {}, NDCG: {}, At Epoch {}".format(best_hr, best_NDCG, best_epoch))
+        print("Training complete!")
+
+    def run_epoch(self, sess, verbose=10):
+        train_u, train_i, train_r = self.dataSet.getInstances(self.train, self.negNum)
+        train_len = len(train_u)
+        shuffled_idx = np.random.permutation(np.arange(train_len))
+        train_u = train_u[shuffled_idx]
+        train_i = train_i[shuffled_idx]
+        train_r = train_r[shuffled_idx]
+
+        num_batches = len(train_u) // self.batchSize + 1
+
+        losses = []
+        for i in range(num_batches):
+            min_idx = i * self.batchSize
+            max_idx = np.min([train_len, (i+1)*self.batchSize])
+            train_u_batch = train_u[min_idx: max_idx]
+            train_i_batch = train_i[min_idx: max_idx]
+            train_r_batch = train_r[min_idx: max_idx]
+
+            feed_dict = self.create_feed_dict(train_u_batch, train_i_batch, train_r_batch)
+            _, tmp_loss = sess.run([self.train_step, self.loss], feed_dict=feed_dict)
+            losses.append(tmp_loss)
+            if verbose and i % verbose == 0:
+                sys.stdout.write('\r{} / {} : loss = {}'.format(
+                    i, num_batches, np.mean(losses[-verbose:])
+                ))
+                sys.stdout.flush()
+        loss = np.mean(losses)
+        print("\nMean loss in this epoch is: {}".format(loss))
+        return loss
+
+    def create_feed_dict(self, u, i, r=None, drop=None):
+        return {self.user: u,
+                self.item: i,
+                self.rate: r,
+                self.drop: drop}
+
+    def evaluate(self, sess, topK):
+        def getHitRatio(ranklist, targetItem):
+            for item in ranklist:
+                if item == targetItem:
+                    return 1
+            return 0
+        def getNDCG(ranklist, targetItem):
+            for i in range(len(ranklist)):
+                item = ranklist[i]
+                if item == targetItem:
+                    return math.log(2) / math.log(i+2)
+            return 0
+
+
+        hr =[]
+        NDCG = []
+        testUser = self.testNeg[0]
+        testItem = self.testNeg[1]
+        for i in range(len(testUser)):
+            target = testItem[i][0]
+            feed_dict = self.create_feed_dict(testUser[i], testItem[i])
+            predict = sess.run(self.y_, feed_dict=feed_dict)
+
+            item_score_dict = {}
+
+            for j in range(len(testItem[i])):
+                item = testItem[i][j]
+                item_score_dict[item] = predict[j]
+
+            ranklist = heapq.nlargest(topK, item_score_dict, key=item_score_dict.get)
+
+            tmp_hr = getHitRatio(ranklist, target)
+            tmp_NDCG = getNDCG(ranklist, target)
+            hr.append(tmp_hr)
+            NDCG.append(tmp_NDCG)
+        return np.mean(hr), np.mean(NDCG)
+
+if __name__ == '__main__':
+    main()