def __init__(self,
n_uid,
n_mid,
n_cat,
EMBEDDING_DIM,
HIDDEN_SIZE,
ATTENTION_SIZE,
use_negsampling=False):
super(Model_NARM,
self).__init__(n_uid, n_mid, n_cat, EMBEDDING_DIM, HIDDEN_SIZE,
ATTENTION_SIZE, use_negsampling)
# RNN layer(-s) - 1
with tf.name_scope('rnn_1'):
rnn_outputs1, final_state1 = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_his_eb,
sequence_length=self.seq_len_ph,
dtype=tf.float32,
scope="lstm1")
tf.summary.histogram('LSTM_outputs1', rnn_outputs1)
# RNN layer(-s) - 2
with tf.name_scope('rnn_2'):
rnn_outputs2, final_state2 = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_his_eb,
sequence_length=self.seq_len_ph,
dtype=tf.float32,
scope="lstm2")
tf.summary.histogram('LSTM_outputs2', rnn_outputs2)
# Attention layer
with tf.name_scope('Attention_layer'):
att_outputs, alphas = din_fcn_attention(final_state1[1],
rnn_outputs2,
ATTENTION_SIZE,
self.mask,
softmax_stag=1,
stag='1_1',
mode='LIST',
return_alphas=True)
tf.summary.histogram('alpha_outputs', alphas)
att_fea = tf.reduce_sum(att_outputs, 1)
inp = tf.concat([final_state1[1], att_fea, self.item_eb], 1)
self.build_fcn_net(inp, use_dice=True)
def __init__(self, user_number, item_number, cate_number, EMBEDDING_DIM,
HIDDEN_SIZE, ATTENTION_SIZE):
super(Model_NARM,
self).__init__(user_number, item_number, cate_number,
EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE)
# RNN layer(1)
with tf.name_scope('rnn_1'):
rnn_outputs1, final_state1 = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_history_embedding,
sequence_length=self.sequence_length,
dtype=tf.float32,
scope="lstm_1")
tf.summary.histogram('LSTM_outputs1', rnn_outputs1)
# RNN layer(2)
with tf.name_scope('rnn_2'):
rnn_outputs2, final_state2 = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_history_embedding,
sequence_length=self.sequence_length,
dtype=tf.float32,
scope="lstm_2")
tf.summary.histogram('LSTM_outputs2', rnn_outputs2)
# Attention layer
with tf.name_scope('Attention_layer'):
att_outputs, alphas = attention_FCN(final_state1[1],
rnn_outputs2,
ATTENTION_SIZE,
self.mask,
softmax_stag=1,
stag='1_1',
mode='LIST',
return_alphas=True)
tf.summary.histogram('alpha_outputs', alphas)
att_fea = tf.reduce_sum(att_outputs, 1)
last_inps = tf.concat(
[final_state1[1], att_fea, self.target_item_embedding], 1)
self.fcn_net(last_inps, use_dice=True)
def __init__(self,
n_uid,
n_mid,
n_cat,
EMBEDDING_DIM,
HIDDEN_SIZE,
ATTENTION_SIZE,
use_negsampling=False):
super(Model_LSTMPP,
self).__init__(n_uid, n_mid, n_cat, EMBEDDING_DIM, HIDDEN_SIZE,
ATTENTION_SIZE, use_negsampling)
# Attention layer
with tf.name_scope('Attention_layer_1'):
att_outputs1, alphas1 = attention_HAN(
self.item_his_eb,
attention_size=ATTENTION_SIZE,
return_alphas=True)
att_fea1 = tf.reduce_sum(att_outputs1, 1)
tf.summary.histogram('att_fea1', att_fea1)
# RNN layer(-s)
with tf.name_scope('rnn_1'):
rnn_outputs, final_state1 = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_his_eb,
sequence_length=self.seq_len_ph,
dtype=tf.float32,
scope="lstm1")
tf.summary.histogram('LSTM_outputs', rnn_outputs)
#alpha
with tf.name_scope('User_alpha'):
concat_all = tf.concat([
self.item_eb, att_fea1, final_state1[1],
tf.expand_dims(self.time_now_his_batch_ph[:, -1], -1)
], 1)
concat_att1 = tf.layers.dense(concat_all,
80,
activation=tf.nn.sigmoid,
name='concat_att1')
concat_att2 = tf.layers.dense(concat_att1,
40,
activation=tf.nn.sigmoid,
name='concat_att2')
user_alpha = tf.layers.dense(concat_att2,
1,
activation=tf.nn.sigmoid,
name='concat_att3')
user_embed = att_fea1 * user_alpha + final_state1[1] * (1.0 -
user_alpha)
inp = tf.concat([self.item_eb, user_embed], 1)
self.build_fcn_net(inp, use_dice=True)
def __init__(self, user_number, item_number, cate_number, EMBEDDING_DIM,
HIDDEN_SIZE, ATTENTION_SIZE):
super(Model_LSTMPP,
self).__init__(user_number, item_number, cate_number,
EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE)
# Attention layer
with tf.name_scope('Attention_layer'):
att_outputs, alphas = attention_HAN(self.item_history_embedding,
attention_size=ATTENTION_SIZE,
return_alphas=True)
att_fea = tf.reduce_sum(att_outputs, 1)
tf.summary.histogram('att_fea', att_fea)
# RNN layer
with tf.name_scope('rnn'):
rnn_outputs, final_state = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_history_embedding,
sequence_length=self.sequence_length,
dtype=tf.float32,
scope="lstm")
tf.summary.histogram('LSTM_outputs', rnn_outputs)
# alpha
with tf.name_scope('User_alpha'):
concat_all = tf.concat([
self.target_item_embedding, att_fea, final_state[1],
tf.expand_dims(self.timenow_history[:, -1], -1)
], 1)
concat_att1 = tf.layers.dense(concat_all,
80,
activation=tf.nn.sigmoid,
name='concat_att1')
concat_att2 = tf.layers.dense(concat_att1,
40,
activation=tf.nn.sigmoid,
name='concat_att2')
user_alpha = tf.layers.dense(concat_att2,
1,
activation=tf.nn.sigmoid,
name='concat_att3')
user_embed = att_fea * user_alpha + final_state[1] * (1.0 -
user_alpha)
last_inps = tf.concat([self.target_item_embedding, user_embed], 1)
self.fcn_net(last_inps, use_dice=True)
def __init__(self, user_number, item_number, cate_number, EMBEDDING_DIM,
HIDDEN_SIZE, ATTENTION_SIZE):
super(Model_LSTM,
self).__init__(user_number, item_number, cate_number,
EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE)
# RNN layer
with tf.name_scope('rnn'):
rnn_outputs, final_state = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_history_embedding,
sequence_length=self.sequence_length,
dtype=tf.float32,
scope="lstm")
tf.summary.histogram('LSTM_outputs', rnn_outputs)
last_inps = tf.concat([self.target_item_embedding, final_state[1]], 1)
self.fcn_net(last_inps, use_dice=False)
def __init__(self,
n_uid,
n_mid,
n_cat,
EMBEDDING_DIM,
HIDDEN_SIZE,
ATTENTION_SIZE,
use_negsampling=False):
super(Model_LSTM,
self).__init__(n_uid, n_mid, n_cat, EMBEDDING_DIM, HIDDEN_SIZE,
ATTENTION_SIZE, use_negsampling)
# RNN layer(-s)
with tf.name_scope('rnn_1'):
rnn_outputs, final_state1 = dynamic_rnn(
LSTMCell(HIDDEN_SIZE),
inputs=self.item_his_eb,
sequence_length=self.seq_len_ph,
dtype=tf.float32,
scope="lstm1")
tf.summary.histogram('LSTM_outputs', rnn_outputs)
inp = tf.concat([self.item_eb, final_state1[1]], 1)
self.build_fcn_net(inp, use_dice=True)
def lstm_cell(self, size):
return LSTMCell(size, initializer=tf.orthogonal_initializer())