基于深度学习的推荐算法实现（以MovieLens 1M数据 为例）_使用movielens 1m的模型

# 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'

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

def get_user_feature_layer(uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer):
    with tf.name_scope("user_fc"):
        #第一层全连接
        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)
 
        #第二层全连接
        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

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

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

def get_movie_cnn_layer(movie_titles):
    #从嵌入矩阵中得到电影名对应的各个单词的嵌入向量
    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)
 
    #对文本嵌入层使用不同尺寸的卷积核做卷积和最大池化
    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层
    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

def get_movie_feature_layer(movie_id_embed_layer, movie_categories_embed_layer, dropout_layer):
    with tf.name_scope("movie_fc"):
        #第一层全连接
        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)
 
        #第二层全连接
        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

tf.reset_default_graph()
train_graph = tf.Graph()
with train_graph.as_default():
    #获取输入占位符
    uid, user_gender, user_age, user_job, movie_id, movie_categories, movie_titles, targets, lr, dropout_keep_prob = get_inputs()
    #获取User的4个嵌入向量
    uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer = get_user_embedding(uid, user_gender, user_age, user_job)
    #得到用户特征
    user_combine_layer, user_combine_layer_flat = get_user_feature_layer(uid_embed_layer, gender_embed_layer, age_embed_layer, job_embed_layer)
    #获取电影ID的嵌入向量
    movie_id_embed_layer = get_movie_id_embed_layer(movie_id)
    #获取电影类型的嵌入向量
    movie_categories_embed_layer = get_movie_categories_layers(movie_categories)
    #获取电影名的特征向量
    pool_layer_flat, dropout_layer = get_movie_cnn_layer(movie_titles)
    #得到电影特征
    movie_combine_layer, movie_combine_layer_flat = get_movie_feature_layer(movie_id_embed_layer, 
                                                                                movie_categories_embed_layer, 
                                                                                dropout_layer)
    #计算出评分，要注意两个不同的方案，inference的名字（name值）是不一样的，后面做推荐时要根据name取得tensor
    with tf.name_scope("inference"):
        #将用户特征和电影特征作为输入，经过全连接，输出一个值的方案
#         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")
        #简单的将用户特征和电影特征做矩阵乘法得到一个预测评分
        inference = tf.matmul(user_combine_layer_flat, tf.transpose(movie_combine_layer_flat))
 
    with tf.name_scope("loss"):
        # MSE损失，将计算值回归到评分
        cost = tf.losses.mean_squared_error(targets, inference )
        loss = tf.reduce_mean(cost)
    # 优化损失 
#     train_op = tf.train.AdamOptimizer(lr).minimize(loss)  #cost
    global_step = tf.Variable(0, name="global_step", trainable=False)
    optimizer = tf.train.AdamOptimizer(lr)
    gradients = optimizer.compute_gradients(loss)  #cost
    train_op = optimizer.apply_gradients(gradients, global_step=global_step)

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import time
import datetime
 
losses = {'train':[], 'test':[]}
 
with tf.Session(graph=train_graph) as sess:
 
    #搜集数据给tensorBoard用
    # Keep track of gradient values and sparsity
    grad_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 summaries
    timestamp = 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 accuracy
    loss_summary = tf.summary.scalar("loss", loss)
 
    # Train Summaries
    train_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 summaries
    inference_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):
 
        #将数据集分成训练集和测试集，随机种子不固定
        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)
 
        #训练的迭代，保存训练损失
        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_keep
                lr: learning_rate}
 
            step, train_loss, summaries, _ = sess.run([global_step, loss, train_summary_op, train_op], feed)  #cost
            losses['train'].append(train_loss)
            train_summary_writer.add_summary(summaries, step)  #
 
            # Show every <show_every_n_batches> batches
            if (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))
 
        #使用测试数据的迭代
        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
 
            #保存测试损失
            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 Model
    saver.save(sess, save_dir)  #, global_step=epoch_i
    print('Model Trained and Saved')

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")
    #两种不同计算预测评分的方案使用不同的name获取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

import random
 
def recommend_other_favorite_movie(movie_id_val, top_k = 20):
    loaded_graph = tf.Graph()  #
    with tf.Session(graph=loaded_graph) as sess:  #
        # Load saved model
        loader = 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("您看的电影是：{}".format(movies_orig[movieid2idx[movie_id_val]]))
 
        print("喜欢看这个电影的人是：{}".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("喜欢看这个电影的人还喜欢看：")
 
        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