請安裝TensorFlow1.0，Python3.5
項目地址：https://github.com/xiaobingchan/movie_recommender

前言
本項目使用文本卷積神經網絡，并使用MovieLens數據集完成電影推薦的任務。
推薦系統在日常的網絡應用中無處不在，比如網上購物、網上買書、新聞app、社交網絡、音樂網站、電影網站等等等等，有人的地方就有推薦。根據個人的喜好，相同喜好人群的習慣等信息進行個性化的內容推薦。比如打開新聞類的app，因為有了個性化的內容，每個人看到的新聞首頁都是不一樣的。
這當然是很有用的，在信息爆炸的今天，獲取信息的途徑和方式多種多樣，人們花費時間最多的不再是去哪獲取信息，而是要在眾多的信息中尋找自己感興趣的，這就是信息超載問題。為了解決這個問題，推薦系統應運而生。
協同過濾是推薦系統應用較廣泛的技術，該方法搜集用戶的歷史記錄、個人喜好等信息，計算與其他用戶的相似度，利用相似用戶的評價來預測目標用戶對特定項目的喜好程度。優點是會給用戶推薦未瀏覽過的項目，缺點呢，對于新用戶來說，沒有任何與商品的交互記錄和個人喜好等信息，存在冷啟動問題，導致模型無法找到相似的用戶或商品。
為了解決冷啟動的問題，通常的做法是對于剛注冊的用戶，要求用戶先選擇自己感興趣的話題、群組、商品、性格、喜歡的音樂類型等信息，比如豆瓣FM：

先來看看數據
本項目使用的是MovieLens 1M 數據集，包含6000個用戶在近4000部電影上的1億條評論。
數據集分為三個文件：用戶數據users.dat，電影數據movies.dat和評分數據ratings.dat。

用戶數據
分別有用戶ID、性別、年齡、職業ID和郵編等字段。

數據中的格式：UserID::Gender::Age::Occupation::Zip-code

Gender is denoted by a “M” for male and “F” for female
Age is chosen from the following ranges:

1: “Under 18”
18: “18-24”
25: “25-34”
35: “35-44”
45: “45-49”
50: “50-55”
56: “56+”
Occupation is chosen from the following choices:

0: “other” or not specified
1: “academic/educator”
2: “artist”
3: “clerical/admin”
4: “college/grad student”
5: “customer service”
6: “doctor/health care”
7: “executive/managerial”
8: “farmer”
9: “homemaker”
10: “K-12 student”
11: “lawyer”
12: “programmer”
13: “retired”
14: “sales/marketing”
15: “scientist”
16: “self-employed”
17: “technician/engineer”
18: “tradesman/craftsman”
19: “unemployed”
20: “writer”

其中UserID、Gender、Age和Occupation都是類別字段，其中郵編字段是我們不使用的。
電影數據
分別有電影ID、電影名和電影風格等字段。

數據中的格式：MovieID::Title::Genres

Titles are identical to titles provided by the IMDB (including
year of release)
Genres are pipe-separated and are selected from the following genres:

Action
Adventure
Animation
Children’s
Comedy
Crime
Documentary
Drama
Fantasy
Film-Noir
Horror
Musical
Mystery
Romance
Sci-Fi
Thriller
War
Western

MovieID是類別字段，Title是文本，Genres也是類別字段
評分數據
分別有用戶ID、電影ID、評分和時間戳等字段。

數據中的格式：UserID::MovieID::Rating::Timestamp

UserIDs range between 1 and 6040
MovieIDs range between 1 and 3952
Ratings are made on a 5-star scale (whole-star ratings only)
Timestamp is represented in seconds since the epoch as returned by time(2)
Each user has at least 20 ratings

評分字段Rating就是我們要學習的targets，時間戳字段我們不使用。
說說數據預處理
UserID、Occupation和MovieID不用變。
Gender字段：需要將‘F’和‘M’轉換成0和1。
Age字段：要轉成7個連續數字0~6。
Genres字段：是分類字段，要轉成數字。首先將Genres中的類別轉成字符串到數字的字典，然后再將每個電影的Genres字段轉成數字列表，因為有些電影是多個Genres的組合。
Title字段：處理方式跟Genres字段一樣，首先創建文本到數字的字典，然后將Title中的描述轉成數字的列表。另外Title中的年份也需要去掉。
Genres和Title字段需要將長度統一，這樣在神經網絡中方便處理。空白部分用‘< PAD >’對應的數字填充。
數據預處理的代碼可以在項目中找到：load_data函數

模型設計

通過研究數據集中的字段類型，我們發現有一些是類別字段，通常的處理是將這些字段轉成one hot編碼，但是像UserID、MovieID這樣的字段就會變成非常的稀疏，輸入的維度急劇膨脹，這是我們不愿意見到的，畢竟我這小筆記本不像大廠動輒能處理數以億計維度的輸入：）
所以在預處理數據時將這些字段轉成了數字，我們用這個數字當做嵌入矩陣的索引，在網絡的第一層使用了嵌入層，維度是（N，32）和（N，16）。
電影類型的處理要多一步，有時一個電影有多個電影類型，這樣從嵌入矩陣索引出來是一個（n，32）的矩陣，因為有多個類型嘛，我們要將這個矩陣求和，變成（1，32）的向量。
電影名的處理比較特殊，沒有使用循環神經網絡，而是用了文本卷積網絡，下文會進行說明。
從嵌入層索引出特征以后，將各特征傳入全連接層，將輸出再次傳入全連接層，最終分別得到（1，200）的用戶特征和電影特征兩個特征向量。
我們的目的就是要訓練出用戶特征和電影特征，在實現推薦功能時使用。得到這兩個特征以后，就可以選擇任意的方式來擬合評分了。我使用了兩種方式，一個是上圖中畫出的將兩個特征做向量乘法，將結果與真實評分做回歸，采用MSE優化損失。因為本質上這是一個回歸問題，另一種方式是，將兩個特征作為輸入，再次傳入全連接層，輸出一個值，將輸出值回歸到真實評分，采用MSE優化損失。
實際上第二個方式的MSE loss在0.8附近，第一個方式在1附近，5次迭代的結果。

文本卷積網絡
網絡看起來像下面這樣

圖片來自Kim Yoon的論文：Convolutional Neural Networks for Sentence Classification

將卷積神經網絡用于文本的文章建議你閱讀Understanding Convolutional Neural Networks for NLP
網絡的第一層是詞嵌入層，由每一個單詞的嵌入向量組成的嵌入矩陣。下一層使用多個不同尺寸（窗口大小）的卷積核在嵌入矩陣上做卷積，窗口大小指的是每次卷積覆蓋幾個單詞。這里跟對圖像做卷積不太一樣，圖像的卷積通常用2x2、3x3、5x5之類的尺寸，而文本卷積要覆蓋整個單詞的嵌入向量，所以尺寸是（單詞數，向量維度），比如每次滑動3個，4個或者5個單詞。第三層網絡是max pooling得到一個長向量，最后使用dropout做正則化，最終得到了電影Title的特征。

核心代碼講解
完整代碼請見項目

#嵌入矩陣的維度
embed_dim = 32
#用戶ID個數
uid_max = max(features.take(0,1)) + 1 # 6040
#性別個數
gender_max = max(features.take(2,1)) + 1 # 1 + 1 = 2
#年齡類別個數
age_max = max(features.take(3,1)) + 1 # 6 + 1 = 7
#職業個數
job_max = max(features.take(4,1)) + 1# 20 + 1 = 21

#電影ID個數
movie_id_max = max(features.take(1,1)) + 1 # 3952
#電影類型個數
movie_categories_max = max(genres2int.values()) + 1 # 18 + 1 = 19
#電影名單詞個數
movie_title_max = len(title_set) # 5216

#對電影類型嵌入向量做加和操作的標志，考慮過使用mean做平均，但是沒實現mean
combiner = "sum"

#電影名長度
sentences_size = title_count # = 15
#文本卷積滑動窗口，分別滑動2, 3, 4, 5個單詞
window_sizes = {2, 3, 4, 5}
#文本卷積核數量
filter_num = 8

#電影ID轉下標的字典，數據集中電影ID跟下標不一致，比如第5行的數據電影ID不一定是5
movieid2idx = {val[0]:i for i, val in enumerate(movies.values)}

超參
# Number of Epochs
num_epochs = 5
# Batch Size
batch_size = 256

dropout_keep = 0.5
# Learning Rate
learning_rate = 0.0001
# Show stats for every n number of batches
show_every_n_batches = 20

save_dir = './save'

輸入
定義輸入的占位符

def get_inputs():
? ? uid = tf.placeholder(tf.int32, [None, 1], name="uid")
? ? user_gender = tf.placeholder(tf.int32, [None, 1], name="user_gender")
? ? user_age = tf.placeholder(tf.int32, [None, 1], name="user_age")
? ? user_job = tf.placeholder(tf.int32, [None, 1], name="user_job")

? ? movie_id = tf.placeholder(tf.int32, [None, 1], name="movie_id")
? ? movie_categories = tf.placeholder(tf.int32, [None, 18], name="movie_categories")
? ? movie_titles = tf.placeholder(tf.int32, [None, 15], name="movie_titles")
? ? targets = tf.placeholder(tf.int32, [None, 1], name="targets")
? ? LearningRate = tf.placeholder(tf.float32, name = "LearningRate")
? ? dropout_keep_prob = tf.placeholder(tf.float32, name = "dropout_keep_prob")
? ? return uid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, LearningRate, dropout_keep_prob

構建神經網絡
定義User的嵌入矩陣
def get_user_embedding(uid, user_gender, user_age, user_job):
? ? with tf.name_scope("user_embedding"):
? ? ? ? uid_embed_matrix = tf.Variable(tf.random_uniform([uid_max, embed_dim], -1, 1), name = "uid_embed_matrix")
? ? ? ? uid_embed_layer = tf.nn.embedding_lookup(uid_embed_matrix, uid, name = "uid_embed_layer")

? ? ? ? gender_embed_matrix = tf.Variable(tf.random_uniform([gender_max, embed_dim // 2], -1, 1), name= "gender_embed_matrix")
? ? ? ? gender_embed_layer = tf.nn.embedding_lookup(gender_embed_matrix, user_gender, name = "gender_embed_layer")

? ? ? ? age_embed_matrix = tf.Variable(tf.random_uniform([age_max, embed_dim // 2], -1, 1), name="age_embed_matrix")
? ? ? ? age_embed_layer = tf.nn.embedding_lookup(age_embed_matrix, user_age, name="age_embed_layer")

? ? ? ? job_embed_matrix = tf.Variable(tf.random_uniform([job_max, embed_dim // 2], -1, 1), name = "job_embed_matrix")
? ? ? ? job_embed_layer = tf.nn.embedding_lookup(job_embed_matrix, user_job, name = "job_embed_layer")
? ? return uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer

將User的嵌入矩陣一起全連接生成User的特征
def get_user_feature_layer(uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer):
? ? with tf.name_scope("user_fc"):
? ? ? ? #第一層全連接
? ? ? ? uid_fc_layer = tf.layers.dense(uid_embed_layer, embed_dim, name = "uid_fc_layer", activation=tf.nn.relu)
? ? ? ? gender_fc_layer = tf.layers.dense(gender_embed_layer, embed_dim, name = "gender_fc_layer", activation=tf.nn.relu)
? ? ? ? age_fc_layer = tf.layers.dense(age_embed_layer, embed_dim, name ="age_fc_layer", activation=tf.nn.relu)
? ? ? ? job_fc_layer = tf.layers.dense(job_embed_layer, embed_dim, name = "job_fc_layer", activation=tf.nn.relu)

? ? ? ? #第二層全連接
? ? ? ? user_combine_layer = tf.concat([uid_fc_layer, gender_fc_layer, age_fc_layer, job_fc_layer], 2) ?#(?, 1, 128)
? ? ? ? user_combine_layer = tf.contrib.layers.fully_connected(user_combine_layer, 200, tf.tanh) ?#(?, 1, 200)

? ? ? ? user_combine_layer_flat = tf.reshape(user_combine_layer, [-1, 200])
? ? return user_combine_layer, user_combine_layer_flat

定義Movie ID的嵌入矩陣
def get_movie_id_embed_layer(movie_id):
? ? with tf.name_scope("movie_embedding"):
? ? ? ? movie_id_embed_matrix = tf.Variable(tf.random_uniform([movie_id_max, embed_dim], -1, 1), name = "movie_id_embed_matrix")
? ? ? ? movie_id_embed_layer = tf.nn.embedding_lookup(movie_id_embed_matrix, movie_id, name = "movie_id_embed_layer")
? ? return movie_id_embed_layer

對電影類型的多個嵌入向量做加和
def get_movie_categories_layers(movie_categories):
? ? with tf.name_scope("movie_categories_layers"):
? ? ? ? movie_categories_embed_matrix = tf.Variable(tf.random_uniform([movie_categories_max, embed_dim], -1, 1), name = "movie_categories_embed_matrix")
? ? ? ? movie_categories_embed_layer = tf.nn.embedding_lookup(movie_categories_embed_matrix, movie_categories, name = "movie_categories_embed_layer")
? ? ? ? if combiner == "sum":
? ? ? ? ? ? movie_categories_embed_layer = tf.reduce_sum(movie_categories_embed_layer, axis=1, keep_dims=True)
? ? # ? ? elif combiner == "mean":

? ? return movie_categories_embed_layer

Movie Title的文本卷積網絡實現
def get_movie_cnn_layer(movie_titles):
? ? #從嵌入矩陣中得到電影名對應的各個單詞的嵌入向量
? ? with tf.name_scope("movie_embedding"):
? ? ? ? movie_title_embed_matrix = tf.Variable(tf.random_uniform([movie_title_max, embed_dim], -1, 1), name = "movie_title_embed_matrix")
? ? ? ? movie_title_embed_layer = tf.nn.embedding_lookup(movie_title_embed_matrix, movie_titles, name = "movie_title_embed_layer")
? ? ? ? movie_title_embed_layer_expand = tf.expand_dims(movie_title_embed_layer, -1)

? ? #對文本嵌入層使用不同尺寸的卷積核做卷積和最大池化
? ? pool_layer_lst = []
? ? for window_size in window_sizes:
? ? ? ? with tf.name_scope("movie_txt_conv_maxpool_{}".format(window_size)):
? ? ? ? ? ? filter_weights = tf.Variable(tf.truncated_normal([window_size, embed_dim, 1, filter_num],stddev=0.1),name = "filter_weights")
? ? ? ? ? ? filter_bias = tf.Variable(tf.constant(0.1, shape=[filter_num]), name="filter_bias")

? ? ? ? ? ? conv_layer = tf.nn.conv2d(movie_title_embed_layer_expand, filter_weights, [1,1,1,1], padding="VALID", name="conv_layer")
? ? ? ? ? ? relu_layer = tf.nn.relu(tf.nn.bias_add(conv_layer,filter_bias), name ="relu_layer")

? ? ? ? ? ? maxpool_layer = tf.nn.max_pool(relu_layer, [1,sentences_size - window_size + 1 ,1,1], [1,1,1,1], padding="VALID", name="maxpool_layer")
? ? ? ? ? ? pool_layer_lst.append(maxpool_layer)

? ? #Dropout層
? ? with tf.name_scope("pool_dropout"):
? ? ? ? pool_layer = tf.concat(pool_layer_lst, 3, name ="pool_layer")
? ? ? ? max_num = len(window_sizes) * filter_num
? ? ? ? pool_layer_flat = tf.reshape(pool_layer , [-1, 1, max_num], name = "pool_layer_flat")? ? ? ? dropout_layer = tf.nn.dropout(pool_layer_flat, dropout_keep_prob, name = "dropout_layer")
? ? return pool_layer_flat, dropout_layer

將Movie的各個層一起做全連接

def get_movie_feature_layer(movie_id_embed_layer, movie_categories_embed_layer, dropout_layer):with tf.name_scope("movie_fc"):#第一層全連接movie_id_fc_layer = tf.layers.dense(movie_id_embed_layer, embed_dim, name = "movie_id_fc_layer", activation=tf.nn.relu)movie_categories_fc_layer = tf.layers.dense(movie_categories_embed_layer, embed_dim, name = "movie_categories_fc_layer", activation=tf.nn.relu)#第二層全連接movie_combine_layer = tf.concat([movie_id_fc_layer, movie_categories_fc_layer, dropout_layer], 2) ?#(?, 1, 96)movie_combine_layer = tf.contrib.layers.fully_connected(movie_combine_layer, 200, tf.tanh) ?#(?, 1, 200)movie_combine_layer_flat = tf.reshape(movie_combine_layer, [-1, 200])return movie_combine_layer, movie_combine_layer_flat

構建計算圖

tf.reset_default_graph() train_graph = tf.Graph() with train_graph.as_default():#獲取輸入占位符uid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, lr, dropout_keep_prob = get_inputs()#獲取User的4個嵌入向量uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer = get_user_embedding(uid, user_gender, user_age, user_job)#得到用戶特征user_combine_layer, user_combine_layer_flat = get_user_feature_layer(uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer)#獲取電影ID的嵌入向量movie_id_embed_layer = get_movie_id_embed_layer(movie_id)#獲取電影類型的嵌入向量movie_categories_embed_layer = get_movie_categories_layers(movie_categories)#獲取電影名的特征向量pool_layer_flat, dropout_layer = get_movie_cnn_layer(movie_titles)#得到電影特征movie_combine_layer, movie_combine_layer_flat = get_movie_feature_layer(movie_id_embed_layer,?movie_categories_embed_layer,?dropout_layer)#計算出評分，要注意兩個不同的方案，inference的名字（name值）是不一樣的，后面做推薦時要根據name取得tensorwith tf.name_scope("inference"):#將用戶特征和電影特征作為輸入，經過全連接，輸出一個值的方案 # ? ? ? ? inference_layer = tf.concat([user_combine_layer_flat, movie_combine_layer_flat], 1) ?#(?, 200) # ? ? ? ? inference = tf.layers.dense(inference_layer, 1, # ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),? # ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? kernel_regularizer=tf.nn.l2_loss, name="inference")#簡單的將用戶特征和電影特征做矩陣乘法得到一個預測評分inference = tf.matmul(user_combine_layer_flat, tf.transpose(movie_combine_layer_flat))? ? with tf.name_scope("loss"):# MSE損失，將計算值回歸到評分cost = tf.losses.mean_squared_error(targets, inference )loss = tf.reduce_mean(cost)# 優化損失? # ? ? train_op = tf.train.AdamOptimizer(lr).minimize(loss) ?#costglobal_step = tf.Variable(0, name="global_step", trainable=False)optimizer = tf.train.AdamOptimizer(lr)gradients = optimizer.compute_gradients(loss) ?#costtrain_op = optimizer.apply_gradients(gradients, global_step=global_step)

訓練網絡

%matplotlib inline %config InlineBackend.figure_format = 'retina' import matplotlib.pyplot as plt import time import datetimelosses = {'train':[], 'test':[]}with tf.Session(graph=train_graph) as sess:#搜集數據給tensorBoard用# Keep track of gradient values and sparsitygrad_summaries = []for g, v in gradients:if g is not None:grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name.replace(':', '_')), g)sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name.replace(':', '_')), tf.nn.zero_fraction(g))grad_summaries.append(grad_hist_summary)grad_summaries.append(sparsity_summary)grad_summaries_merged = tf.summary.merge(grad_summaries)# Output directory for models and summariestimestamp = str(int(time.time()))out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))print("Writing to {}\n".format(out_dir))# Summaries for loss and accuracyloss_summary = tf.summary.scalar("loss", loss)# Train Summariestrain_summary_op = tf.summary.merge([loss_summary, grad_summaries_merged])train_summary_dir = os.path.join(out_dir, "summaries", "train")train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)# Inference summariesinference_summary_op = tf.summary.merge([loss_summary])inference_summary_dir = os.path.join(out_dir, "summaries", "inference")inference_summary_writer = tf.summary.FileWriter(inference_summary_dir, sess.graph)sess.run(tf.global_variables_initializer())saver = tf.train.Saver()for epoch_i in range(num_epochs):#將數據集分成訓練集和測試集，隨機種子不固定train_X,test_X, train_y, test_y = train_test_split(features, ?targets_values, ?test_size = 0.2, ?random_state = 0) ?train_batches = get_batches(train_X, train_y, batch_size)test_batches = get_batches(test_X, test_y, batch_size)#訓練的迭代，保存訓練損失for batch_i in range(len(train_X) // batch_size):x, y = next(train_batches)categories = np.zeros([batch_size, 18])for i in range(batch_size):categories[i] = x.take(6,1)[i]titles = np.zeros([batch_size, sentences_size])for i in range(batch_size):titles[i] = x.take(5,1)[i]feed = {uid: np.reshape(x.take(0,1), [batch_size, 1]),user_gender: np.reshape(x.take(2,1), [batch_size, 1]),user_age: np.reshape(x.take(3,1), [batch_size, 1]),user_job: np.reshape(x.take(4,1), [batch_size, 1]),movie_id: np.reshape(x.take(1,1), [batch_size, 1]),movie_categories: categories, ?#x.take(6,1)movie_titles: titles, ?#x.take(5,1)targets: np.reshape(y, [batch_size, 1]),dropout_keep_prob: dropout_keep, #dropout_keeplr: learning_rate}step, train_loss, summaries, _ = sess.run([global_step, loss, train_summary_op, train_op], feed) ?#costlosses['train'].append(train_loss)train_summary_writer.add_summary(summaries, step) ?## Show every <show_every_n_batches> batchesif (epoch_i * (len(train_X) // batch_size) + batch_i) % show_every_n_batches == 0:time_str = datetime.datetime.now().isoformat()print('{}: Epoch {:>3} Batch {:>4}/{} ? train_loss = {:.3f}'.format(time_str,epoch_i,batch_i,(len(train_X) // batch_size),train_loss))#使用測試數據的迭代for batch_i ?in range(len(test_X) // batch_size):x, y = next(test_batches)categories = np.zeros([batch_size, 18])for i in range(batch_size):categories[i] = x.take(6,1)[i]titles = np.zeros([batch_size, sentences_size])for i in range(batch_size):titles[i] = x.take(5,1)[i]feed = {uid: np.reshape(x.take(0,1), [batch_size, 1]),user_gender: np.reshape(x.take(2,1), [batch_size, 1]),user_age: np.reshape(x.take(3,1), [batch_size, 1]),user_job: np.reshape(x.take(4,1), [batch_size, 1]),movie_id: np.reshape(x.take(1,1), [batch_size, 1]),movie_categories: categories, ?#x.take(6,1)movie_titles: titles, ?#x.take(5,1)targets: np.reshape(y, [batch_size, 1]),dropout_keep_prob: 1,lr: learning_rate}step, test_loss, summaries = sess.run([global_step, loss, inference_summary_op], feed) ?#cost#保存測試損失losses['test'].append(test_loss)inference_summary_writer.add_summary(summaries, step) ?#time_str = datetime.datetime.now().isoformat()if (epoch_i * (len(test_X) // batch_size) + batch_i) % show_every_n_batches == 0:print('{}: Epoch {:>3} Batch {:>4}/{} ? test_loss = {:.3f}'.format(time_str,epoch_i,batch_i,(len(test_X) // batch_size),test_loss))# Save Modelsaver.save(sess, save_dir) ?#, global_step=epoch_iprint('Model Trained and Saved')

在 TensorBoard 中查看可視化結果
獲取 Tensors

def get_tensors(loaded_graph):uid = loaded_graph.get_tensor_by_name("uid:0")user_gender = loaded_graph.get_tensor_by_name("user_gender:0")user_age = loaded_graph.get_tensor_by_name("user_age:0")user_job = loaded_graph.get_tensor_by_name("user_job:0")movie_id = loaded_graph.get_tensor_by_name("movie_id:0")movie_categories = loaded_graph.get_tensor_by_name("movie_categories:0")movie_titles = loaded_graph.get_tensor_by_name("movie_titles:0")targets = loaded_graph.get_tensor_by_name("targets:0")dropout_keep_prob = loaded_graph.get_tensor_by_name("dropout_keep_prob:0")lr = loaded_graph.get_tensor_by_name("LearningRate:0")#兩種不同計算預測評分的方案使用不同的name獲取tensor inference # ? ? inference = loaded_graph.get_tensor_by_name("inference/inference/BiasAdd:0")inference = loaded_graph.get_tensor_by_name("inference/MatMul:0")#movie_combine_layer_flat = loaded_graph.get_tensor_by_name("movie_fc/Reshape:0")user_combine_layer_flat = loaded_graph.get_tensor_by_name("user_fc/Reshape:0")return uid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, lr, dropout_keep_prob, inference, movie_combine_layer_flat, user_combine_layer_flat

指定用戶和電影進行評分
這部分就是對網絡做正向傳播，計算得到預測的評分

def rating_movie(user_id_val, movie_id_val):loaded_graph = tf.Graph() ?#with tf.Session(graph=loaded_graph) as sess: ?## Load saved modelloader = tf.train.import_meta_graph(load_dir + '.meta')loader.restore(sess, load_dir)# Get Tensors from loaded modeluid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, lr, dropout_keep_prob, inference,_, __ = get_tensors(loaded_graph) ?#loaded_graphcategories = np.zeros([1, 18])categories[0] = movies.values[movieid2idx[movie_id_val]][2]titles = np.zeros([1, sentences_size])titles[0] = movies.values[movieid2idx[movie_id_val]][1]feed = {uid: np.reshape(users.values[user_id_val-1][0], [1, 1]),user_gender: np.reshape(users.values[user_id_val-1][1], [1, 1]),user_age: np.reshape(users.values[user_id_val-1][2], [1, 1]),user_job: np.reshape(users.values[user_id_val-1][3], [1, 1]),movie_id: np.reshape(movies.values[movieid2idx[movie_id_val]][0], [1, 1]),movie_categories: categories, ?#x.take(6,1)movie_titles: titles, ?#x.take(5,1)dropout_keep_prob: 1}# Get Predictioninference_val = sess.run([inference], feed) ?return (inference_val)

生成Movie特征矩陣
將訓練好的電影特征組合成電影特征矩陣并保存到本地

loaded_graph = tf.Graph() ?# movie_matrics = [] with tf.Session(graph=loaded_graph) as sess: ?## Load saved modelloader = tf.train.import_meta_graph(load_dir + '.meta')loader.restore(sess, load_dir)# Get Tensors from loaded modeluid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, lr, dropout_keep_prob, _, movie_combine_layer_flat, __ = get_tensors(loaded_graph) ?#loaded_graphfor item in movies.values:categories = np.zeros([1, 18])categories[0] = item.take(2)titles = np.zeros([1, sentences_size])titles[0] = item.take(1)feed = {movie_id: np.reshape(item.take(0), [1, 1]),movie_categories: categories, ?#x.take(6,1)movie_titles: titles, ?#x.take(5,1)dropout_keep_prob: 1}movie_combine_layer_flat_val = sess.run([movie_combine_layer_flat], feed) ?movie_matrics.append(movie_combine_layer_flat_val)pickle.dump((np.array(movie_matrics).reshape(-1, 200)), open('movie_matrics.p', 'wb')) movie_matrics = pickle.load(open('movie_matrics.p', mode='rb'))

生成User特征矩陣
將訓練好的用戶特征組合成用戶特征矩陣并保存到本地

loaded_graph = tf.Graph() ?# users_matrics = [] with tf.Session(graph=loaded_graph) as sess: ?## Load saved modelloader = tf.train.import_meta_graph(load_dir + '.meta')loader.restore(sess, load_dir)# Get Tensors from loaded modeluid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, lr, dropout_keep_prob, _, __,user_combine_layer_flat = get_tensors(loaded_graph) ?#loaded_graphfor item in users.values:feed = {uid: np.reshape(item.take(0), [1, 1]),user_gender: np.reshape(item.take(1), [1, 1]),user_age: np.reshape(item.take(2), [1, 1]),user_job: np.reshape(item.take(3), [1, 1]),dropout_keep_prob: 1}user_combine_layer_flat_val = sess.run([user_combine_layer_flat], feed) ?users_matrics.append(user_combine_layer_flat_val)pickle.dump((np.array(users_matrics).reshape(-1, 200)), open('users_matrics.p', 'wb')) users_matrics = pickle.load(open('users_matrics.p', mode='rb'))

開始推薦電影
使用生產的用戶特征矩陣和電影特征矩陣做電影推薦

推薦同類型的電影
思路是計算當前看的電影特征向量與整個電影特征矩陣的余弦相似度，取相似度最大的top_k個，這里加了些隨機選擇在里面，保證每次的推薦稍稍有些不同。

def recommend_same_type_movie(movie_id_val, top_k = 20):loaded_graph = tf.Graph() ?#with tf.Session(graph=loaded_graph) as sess: ?## Load saved modelloader = tf.train.import_meta_graph(load_dir + '.meta')loader.restore(sess, load_dir)norm_movie_matrics = tf.sqrt(tf.reduce_sum(tf.square(movie_matrics), 1, keep_dims=True))normalized_movie_matrics = movie_matrics / norm_movie_matrics#推薦同類型的電影probs_embeddings = (movie_matrics[movieid2idx[movie_id_val]]).reshape([1, 200])probs_similarity = tf.matmul(probs_embeddings, tf.transpose(normalized_movie_matrics))sim = (probs_similarity.eval())# ? ? results = (-sim[0]).argsort()[0:top_k]# ? ? print(results)print("您看的電影是：{}".format(movies_orig[movieid2idx[movie_id_val]]))print("以下是給您的推薦：")p = np.squeeze(sim)p[np.argsort(p)[:-top_k]] = 0p = p / np.sum(p)results = set()while len(results) != 5:c = np.random.choice(3883, 1, p=p)[0]results.add(c)for val in (results):print(val)print(movies_orig[val])return results

推薦您喜歡的電影
思路是使用用戶特征向量與電影特征矩陣計算所有電影的評分，取評分最高的top_k個，同樣加了些隨機選擇部分。

def recommend_your_favorite_movie(user_id_val, top_k = 10):loaded_graph = tf.Graph() ?#with tf.Session(graph=loaded_graph) as sess: ?## Load saved modelloader = tf.train.import_meta_graph(load_dir + '.meta')loader.restore(sess, load_dir)#推薦您喜歡的電影probs_embeddings = (users_matrics[user_id_val-1]).reshape([1, 200])probs_similarity = tf.matmul(probs_embeddings, tf.transpose(movie_matrics))sim = (probs_similarity.eval())# ? ? print(sim.shape)# ? ? results = (-sim[0]).argsort()[0:top_k]# ? ? print(results)# ? ? sim_norm = probs_norm_similarity.eval()# ? ? print((-sim_norm[0]).argsort()[0:top_k])print("以下是給您的推薦：")p = np.squeeze(sim)p[np.argsort(p)[:-top_k]] = 0p = p / np.sum(p)results = set()while len(results) != 5:c = np.random.choice(3883, 1, p=p)[0]results.add(c)for val in (results):print(val)print(movies_orig[val])return results

看過這個電影的人還看了（喜歡）哪些電影
首先選出喜歡某個電影的top_k個人，得到這幾個人的用戶特征向量。
然后計算這幾個人對所有電影的評分
選擇每個人評分最高的電影作為推薦
同樣加入了隨機選擇

import randomdef recommend_other_favorite_movie(movie_id_val, top_k = 20):loaded_graph = tf.Graph() ?#with tf.Session(graph=loaded_graph) as sess: ?## Load saved modelloader = tf.train.import_meta_graph(load_dir + '.meta')loader.restore(sess, load_dir)probs_movie_embeddings = (movie_matrics[movieid2idx[movie_id_val]]).reshape([1, 200])probs_user_favorite_similarity = tf.matmul(probs_movie_embeddings, tf.transpose(users_matrics))favorite_user_id = np.argsort(probs_user_favorite_similarity.eval())[0][-top_k:]# ? ? print(normalized_users_matrics.eval().shape)# ? ? print(probs_user_favorite_similarity.eval()[0][favorite_user_id])# ? ? print(favorite_user_id.shape)print("您看的電影是：{}".format(movies_orig[movieid2idx[movie_id_val]]))print("喜歡看這個電影的人是：{}".format(users_orig[favorite_user_id-1]))probs_users_embeddings = (users_matrics[favorite_user_id-1]).reshape([-1, 200])probs_similarity = tf.matmul(probs_users_embeddings, tf.transpose(movie_matrics))sim = (probs_similarity.eval())# ? ? results = (-sim[0]).argsort()[0:top_k]# ? ? print(results)# ? ? print(sim.shape)# ? ? print(np.argmax(sim, 1))p = np.argmax(sim, 1)print("喜歡看這個電影的人還喜歡看：")results = set()while len(results) != 5:c = p[random.randrange(top_k)]results.add(c)for val in (results):print(val)print(movies_orig[val])return results

結論
以上就是實現的常用的推薦功能，將網絡模型作為回歸問題進行訓練，得到訓練好的用戶特征矩陣和電影特征矩陣進行推薦。

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總結

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