CFDNN_GMF_01.py

#-------------
# HYPERPARAMS
#-------------

num_neg = 4
latent_features = 8
epochs = 20
batch_size = 256
learning_rate = 0.001


#-------------------------
# TENSORFLOW GRAPH
#-------------------------

graph = tf.Graph()

with graph.as_default():

    # Define input placeholders for user, item and label.
    user = tf.placeholder(tf.int32, shape=(None, 1))
    item = tf.placeholder(tf.int32, shape=(None, 1))
    label = tf.placeholder(tf.int32, shape=(None, 1))

    # User feature embedding
    u_var = tf.Variable(tf.random_normal([len(users), latent_features],
                                         stddev=0.05), name='user_embedding')
    user_embedding = tf.nn.embedding_lookup(u_var, user)

    # Item feature embedding
    i_var = tf.Variable(tf.random_normal([len(items), latent_features],
                                         stddev=0.05), name='item_embedding')
    item_embedding = tf.nn.embedding_lookup(i_var, item)
    
    # Flatten our user and item embeddings.
    user_embedding = tf.keras.layers.Flatten()(user_embedding)
    item_embedding = tf.keras.layers.Flatten()(item_embedding)

    # Multiplying our user and item latent space vectors together 
    prediction_matrix = tf.multiply(user_embedding, item_embedding)

    # Our single neuron output layer
    output_layer = tf.keras.layers.Dense(1, 
            kernel_initializer="lecun_uniform",
            name='output_layer')(prediction_matrix)

    # Our loss function as a binary cross entropy. 
    loss = tf.losses.sigmoid_cross_entropy(label, output_layer)
    
    # Train using the Adam optimizer to minimize our loss.
    opt = tf.train.AdamOptimizer(learning_rate = learning_rate)
    step = opt.minimize(loss)

    # Initialize all tensorflow variables.
    init = tf.global_variables_initializer()

session = tf.Session(config=None, graph=graph)
session.run(init)