def build_model(self):
time_aware_attention = Time_Aware_Attention()
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder'):
self.short_term_intent_temp = self.gru_net_ins.gru_net(hidden_units=self.num_units,
input_data=self.behavior_list_embedding_dense,
input_length=tf.add(self.seq_length, -1))
user_history = self.short_term_intent_temp
self.short_term_intent = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = layer_norm(self.short_term_intent)
short_term_intent4vallina = tf.expand_dims(self.short_term_intent, 1)
with tf.variable_scope('NextItemDecoder'):
hybird_preference = time_aware_attention.vanilla_attention(user_history, short_term_intent4vallina, self.num_units,
self.num_heads, self.num_blocks, self.dropout_rate,is_training=True,
reuse=False,key_length=self.seq_length,
query_length = tf.ones_like(short_term_intent4vallina[:, 0, 0], dtype=tf.int32),
t_querys = tf.expand_dims(self.target[2],1),t_keys = self.time_list,
t_keys_length=self.max_len,t_querys_length=1 )
self.predict_behavior_emb = layer_norm(hybird_preference)
#z = tf.concat([self.short_term_intent, hybird_preference], 1)
#z = tf.layers.dropout(tf.layers.dense(z, self.num_units, activation=tf.nn.relu), rate=self.FLAGS.dropout,
#training=True)
#z = tf.sigmoid(tf.layers.dense(z, 1))
#self.predict_behavior_emb = layer_norm(z * hybird_preference + (1 - z) * self.short_term_intent)
self.output()
def build_model(self):
attention = Attention()
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder'):
self.short_term_intent_temp = self.gru_net_ins.gru_net(hidden_units=self.num_units,
input_data=self.behavior_list_embedding_dense,
input_length=tf.add(self.seq_length, -1))
user_history = self.short_term_intent_temp
self.short_term_intent = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = layer_norm(self.short_term_intent)
short_term_intent4vallina = tf.expand_dims(self.short_term_intent, 1)
with tf.variable_scope('NextItemDecoder'):
hybird_preference = attention.vanilla_attention(user_history, short_term_intent4vallina, self.num_units,
1, 1, self.dropout_rate,is_training=True,
reuse=False,key_length=self.seq_length,
query_length = tf.ones_like(short_term_intent4vallina[:, 0, 0], dtype=tf.int32))
self.predict_behavior_emb = tf.concat([self.short_term_intent,hybird_preference],1)
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.output_concat()
def build_model(self):
time_aware_attention = Time_Aware_Attention()
self.gru_net_ins = GRU()
with tf.variable_scope("UserHistoryEncoder"):
user_history = self.behavior_list_embedding_dense
with tf.variable_scope('ShortTermIntentEncoder'):
self.time_aware_gru_net_input = tf.concat([
self.behavior_list_embedding_dense,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(self.timenow_list, 2)
], 2)
self.short_term_intent_temp = self.gru_net_ins.time_aware_gru_net(
hidden_units=self.num_units,
input_data=self.time_aware_gru_net_input,
input_length=tf.add(self.seq_length, -1),
type='new')
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = layer_norm(self.short_term_intent)
short_term_intent4vallina = tf.expand_dims(self.short_term_intent,
1)
with tf.variable_scope('NextItemDecoder'):
hybird_preference = time_aware_attention.vanilla_attention(
user_history,
short_term_intent4vallina,
self.num_units,
self.num_heads,
self.num_blocks,
self.dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=tf.ones_like(short_term_intent4vallina[:, 0, 0],
dtype=tf.int32),
t_querys=tf.expand_dims(self.target[2], 1),
t_keys=self.time_list,
t_keys_length=self.max_len,
t_querys_length=1)
hybird_preference = layer_norm(hybird_preference)
with tf.variable_scope('Switch'):
self.long_term_prefernce = hybird_preference
self.short_term_intent = self.short_term_intent
self.predict_behavior_emb = layer_norm(self.long_term_prefernce +
self.short_term_intent)
self.output()
def build_model(self):
user_history = self.behavior_list_embedding_dense
external_memory = layer_norm(tf.reduce_sum(user_history,1))
last_click = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=user_history,
positions=self.mask_index-1)
with tf.variable_scope('AttentionNet'):
att_w0 = variable_scope.get_variable("att_w0",
shape=[self.num_units,1],
dtype=user_history.dtype)
att_w1 = variable_scope.get_variable("att_w1",
shape=[self.num_units, self.num_units],
dtype=user_history.dtype)
att_w2 = variable_scope.get_variable("att_w2",
shape=[self.num_units, self.num_units],
dtype=user_history.dtype)
att_w3 = variable_scope.get_variable("att_w3",
shape=[self.num_units, self.num_units],
dtype=user_history.dtype)
att_b = variable_scope.get_variable("att_b",
shape=[ 1,self.num_units],
dtype=user_history.dtype)
a_history= tf.matmul(user_history,att_w1)
a_external_memory = tf.matmul(external_memory, att_w2)
a_last_click = tf.matmul(last_click, att_w3)
att = a_history+ tf.expand_dims(a_external_memory,1)
att = att + tf.expand_dims(a_last_click,1)
att = tf.sigmoid(att)
#att=tf.sigmoid(tf.matmul(user_history,att_w1)+\
#tf.matmul(external_memory,att_w2)+ \
#tf.matmul(last_click, att_w3)+att_b)
att=tf.squeeze(tf.matmul(att,att_w0),2)
ms= tf.matmul(att,user_history)
ms = tf.reduce_sum(ms,1)
with tf.variable_scope('MLPcellA'):
self.hs = tf.layers.dense(ms, self.num_units,
activation=tf.nn.relu, use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-5))
with tf.variable_scope('MLPcellB'):
self.ht = tf.layers.dense(last_click, self.num_units,
activation=tf.nn.relu, use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-5))
self.predict_behavior_emb =layer_norm(self.hs*self.ht)
self.output()
def build_model(self):
self.gru_net_ins = GraphRNN()
self.gated_gnn_model = modified_gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('neighbor_emb', reuse=tf.AUTO_REUSE):
structure_emb = self.gated_gnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
step=self.FLAGS.graph_step
) # batch_size, max_len, num_units * 2
with tf.variable_scope('ShortTermIntentEncoder', reuse=tf.AUTO_REUSE):
grnn_inputs = tf.concat(
[user_behavior_list_embedding, structure_emb], axis=2)
user_behavior_list_embedding = self.gru_net_ins.modified_grnn_net(
hidden_units=self.num_units,
input_data=grnn_inputs,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=user_behavior_list_embedding,
positions=self.mask_index - 1)
self.short_term_intent = self.short_term_intent
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def build_model(self):
num_units = self.FLAGS.num_units
gru_net_ins = GRU()
self.sequence_embedding, self.positive_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.mask_index, self.label_ids, \
self.seq_length, user_embedding, self.time = self.embedding.get_embedding(num_units)
with tf.variable_scope("EnhanceUserPreferenceIntentEncoder"):
user_preference_temp = gru_net_ins.gru_net_initial(hidden_units=num_units,
input_length=self.mask_index,
input_data=self.behavior_embedding_result_dense,
initial_state=user_embedding)
self.user_preference = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_preference_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = layer_norm(self.user_preference)
# self.mf_auc = tf.reduce_mean(tf.to_float((tf.reduce_sum(tf.multiply(tf.expand_dims(self.predict_behavior_emb, 1),
# tf.expand_dims(self.positive_embedding_result_dense, 1) - self.negative_embedding_result_dense), 2)) > 0))
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids, depth=self.embedding.item_count+3, dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(zip(clip_gradients, trainable_params))
self.summery()
def build_model(self):
time_aware_attention = Time_Aware_Attention()
with tf.variable_scope("UserHistoryEncoder"):
user_history = time_aware_attention.self_attention(
enc=self.behavior_list_embedding_dense,
num_units=self.num_units,
num_heads=self.num_heads,
num_blocks=self.num_blocks,
dropout_rate=self.dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length,
t_querys=self.time_list,
t_keys=self.time_list,
t_keys_length=self.max_len,
t_querys_length=self.max_len)
long_term_prefernce = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=user_history,
positions=self.mask_index)
self.predict_behavior_emb = long_term_prefernce
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.output()
def build_model(self):
self.ctsm_model = ContinuousLSTM()
with tf.variable_scope('ShortTermIntentEncoder', reuse=tf.AUTO_REUSE):
time_aware_gru_net_input = tf.concat([
self.behavior_list_embedding_dense,
],
axis=2)
self.short_term_intent_temp = self.ctsm_model.my_lstm_net(
hidden_units=self.num_units,
input_data=time_aware_gru_net_input,
input_length=tf.add(self.seq_length,
-1)) # batch_size, max_len, num_units
emb = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index -
1) #batch_size, num_units
self.predict_behavior_emb = layer_norm(
emb) # batch_size, num_units
self.output()
def build_model(self):
self.gru = GRU()
self.attention = Attention()
self.wgat_model = WGAT()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('WGAT_gnn_encoding',reuse=tf.AUTO_REUSE):
self.gnn_emb = self.wgat_model.generate_graph_emb(init_emb = user_behavior_list_embedding,
key_length =self.seq_length,
num_units = self.num_units ,
mask_adj=self.adj_in,
num_head=self.FLAGS.graph_head,
step=self.FLAGS.graph_step,
dropout_rate=self.dropout_rate,
max_len = self.max_len)
with tf.variable_scope('readout_function',reuse=tf.AUTO_REUSE):
q_star = self.gru_set2set(init_emb=self.gnn_emb,
num_units=self.num_units,
step=self.FLAGS.FGNN_readout_step)
hybrid_preference = tf.layers.dense(q_star, units = self.num_units)
self.predict_behavior_emb = hybrid_preference
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.output()
def build_model(self):
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder'):
timenext_list = self.timelast_list[:, 1:]
zeros = tf.zeros(shape=(self.now_bacth_data_size, 1))
timenext_list = tf.concat([timenext_list, zeros], axis=1)
self.time_aware_gru_net_input = tf.concat([
self.behavior_list_embedding_dense,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(timenext_list, 2)
], 2)
self.short_term_intent_temp = self.gru_net_ins.time_aware_gru_net(
hidden_units=self.num_units,
input_data=self.time_aware_gru_net_input,
input_length=tf.add(self.seq_length, -1),
type='T_Gru_Extend')
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def build_model(self):
print('--------------------num blocks-------------------------'+str(self.num_blocks))
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder',reuse=tf.AUTO_REUSE):
time_aware_gru_net_input = tf.concat([self.behavior_list_embedding_dense,
self.reconsume_lst_embedding,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(self.reconsume_list,2)],
axis=2)
self.short_term_intent_temp = self.gru_net_ins.reconsume_prediction_gru_net(hidden_units=self.num_units,
input_data=time_aware_gru_net_input,
input_length=tf.add(self.seq_length,-1), #
type='new',
scope='gru') # batch_size, max_len, num_units
emb = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width= self.num_units * 2 ,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index -1 )#batch_size, num_units
# self.predict_is_reconsume = short_term_intent[:,-1] # 最后一位是interval batch_size,
short_term_intent = emb[:,:self.num_units]# 前面是h
# short_term_intent = tf.layers.dense(short_term_intent, self.num_units)
self.predict_behavior_emb = layer_norm(short_term_intent) # batch_size, num_units
predict_reconsume_emb = emb[:,self.num_units:]
reconsume_table = self.embedding.reconsume_emb_lookup_table
reconsume_scores = tf.nn.softmax(tf.matmul(predict_reconsume_emb,reconsume_table,transpose_b=True))
self.predict_is_reconsume = reconsume_scores[:,1]
def cosine(q, a):
pooled_len_1 = tf.sqrt(tf.reduce_sum(q * q, 1))
pooled_len_2 = tf.sqrt(tf.reduce_sum(a * a, 1))
pooled_mul_12 = tf.reduce_sum(q * a, 1)
score = tf.div(pooled_mul_12, pooled_len_1 * pooled_len_2 + 1e-8, name="scores")
return score
item_embs = tf.reshape(self.item_list_emb,[-1,self.num_units]) # batch_size * max_len, num_units
predict_target_embs = tf.tile(self.predict_behavior_emb,[self.max_len,1]) # batch_size * max_len, num_units
reconsume_scores = cosine(predict_target_embs,item_embs) # batch_size * max_len
self.reconsume_scores = tf.reshape(reconsume_scores,[-1,self.max_len])
self.output()
def build_model(self):
attention = Attention()
self.gru_net_ins = GRU()
self.ggnn_model = ordered_gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('ggnn_encoding',reuse=tf.AUTO_REUSE):
self.short_term_intent_temp = self.ggnn_model.generate_graph_emb(init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
mask_adj_in=self.mask_adj_in,
mask_adj_out=self.mask_adj_out,
eid_emb_in=self.in_eid_embedding,
eid_emb_out=self.out_eid_embedding,
step=self.FLAGS.graph_step)
# with tf.variable_scope('ShortTermIntentEncoder'):
# self.short_term_intent_temp = self.gru_net_ins.gru_net(hidden_units=self.num_units,
# input_data=self.behavior_list_embedding_dense,
# input_length=tf.add(self.seq_length, -1))
user_history = self.short_term_intent_temp
self.short_term_intent = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = layer_norm(self.short_term_intent)
short_term_intent4vallina = tf.expand_dims(self.short_term_intent, 1)
with tf.variable_scope('NextItemDecoder'):
hybird_preference = attention.vanilla_attention(user_history, short_term_intent4vallina, self.num_units,
1, 1, self.dropout_rate, is_training=True,
reuse=False, key_length=self.seq_length,
query_length=tf.ones_like(
short_term_intent4vallina[:, 0, 0], dtype=tf.int32))
self.predict_behavior_emb = tf.concat([self.short_term_intent, hybird_preference], 1)
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.output_concat()
def build_model(self):
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder'):
self.short_term_intent_temp = self.gru_net_ins.gru_net_initial(hidden_units=self.num_units,
initial_state=self.user_embedding,
input_data=self.behavior_list_embedding_dense,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def build_model(self):
time_aware_attention = Time_Aware_Attention()
with tf.variable_scope("UserHistoryEncoder"):
user_history = time_aware_attention.self_attention(
enc=self.behavior_list_embedding_dense,
num_units=128,
num_heads=self.num_heads,
num_blocks=self.num_blocks,
dropout_rate=self.dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length,
t_querys=self.time_list,
t_keys=self.time_list,
t_keys_length=self.max_len,
t_querys_length=self.max_len)
long_term_prefernce = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_history,
positions=self.mask_index)
self.predict_behavior_emb = long_term_prefernce
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
with tf.name_scope('CrossEntropyLoss'):
l2_norm = tf.add_n([
tf.nn.l2_loss(self.item_list_emb),
tf.nn.l2_loss(self.category_list_emb),
tf.nn.l2_loss(self.position_list_emb)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(
self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.target[0], [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=self.embedding.item_count + 3,
dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
self.loss = regulation_rate * l2_norm + tf.reduce_mean(
self.loss_origin)
tf.summary.scalar('l2_norm', l2_norm)
tf.summary.scalar('Training Loss', self.loss)
tf.summary.scalar('Learning_rate', self.learning_rate)
self.cal_gradient(tf.trainable_variables())
def build_model(self):
self.gru_net_ins = GraphRNN()
self.gated_gnn_model = ordered_gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('neighbor_emb', reuse=tf.AUTO_REUSE):
structure_emb = self.gated_gnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
eid_emb_in=self.in_eid_embedding,
eid_emb_out=self.out_eid_embedding,
mask_adj_in=self.mask_adj_in,
mask_adj_out=self.mask_adj_out,
step=self.FLAGS.graph_step
) # batch_size, max_len, num_units * 2
with tf.variable_scope('ShortTermIntentEncoder'):
# in_emb, out_emb = array_ops.split(value=structure_emb, num_or_size_splits=2, axis=2)
#
# structure_emb = in_emb+out_emb
# structure_emb = tf.layers.dense(structure_emb,units = self.num_units)
grnn_inputs = tf.concat(
[user_behavior_list_embedding, structure_emb], axis=2)
self.short_term_intent_temp = self.gru_net_ins.simple_grnn_net(
hidden_units=self.num_units,
input_data=grnn_inputs,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = self.short_term_intent
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def generate_graph_emb(self,
init_emb,
key_length,
num_units,
mask_adj,
num_head=1,
step=2):
graph_att_model = GraphAttention()
emb = graph_att_model.gat_attention(enc=init_emb,
key_length=key_length,
mask_adj=mask_adj,
num_units=num_units,
num_heads=num_head,
num_blocks=step,
dropout_rate=0.1,
is_training=True,
reuse=None)
return layer_norm(emb)
def build_model(self):
self.gru_net_ins = GRU()
self.ggnn_model = gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('ggnn_encoding'):
gnn_emb = self.ggnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
step=1)
time_aware_attention = Time_Aware_Attention()
with tf.variable_scope("UserHistoryEncoder"):
user_history = time_aware_attention.self_attention(
gnn_emb,
self.num_units,
self.num_heads,
self.num_blocks,
self.dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length,
t_querys=self.time_list,
t_keys=self.time_list,
t_keys_length=self.max_len,
t_querys_length=self.max_len)
long_term_prefernce = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_history,
positions=self.mask_index)
self.predict_behavior_emb = long_term_prefernce
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.output()
def build_model(self):
self.gru_net_ins = GRU()
self.ggnn_model = gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('ggnn_encoding'):
gnn_emb = self.ggnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
step=1)
with tf.variable_scope('ShortTermIntentEncoder'):
timenext_list = self.timelast_list[:, 1:]
zeros = tf.zeros(shape=(self.now_bacth_data_size, 1))
timenext_list = tf.concat([timenext_list, zeros], axis=1)
self.time_aware_gru_net_input = tf.concat([
gnn_emb,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(timenext_list, 2)
], 2)
self.short_term_intent_temp = self.gru_net_ins.time_aware_gru_net(
hidden_units=self.num_units,
input_data=self.time_aware_gru_net_input,
input_length=tf.add(self.seq_length, -1),
type='new')
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def build_model(self):
print('--------------------num blocks-------------------------' +
str(self.num_blocks))
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder', reuse=tf.AUTO_REUSE):
timefirst_lst = tf.reshape(self.time_list[:, 0], [-1, 1])
idx = tf.range(start=1., limit=self.max_len, delta=1)
idx0 = tf.constant([1.])
idx = tf.concat([idx0, idx], axis=0)
avg_interval_lst = (self.time_list - timefirst_lst) / idx
time_aware_gru_net_input = tf.concat([
self.behavior_list_embedding_dense,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(avg_interval_lst, 2)
],
axis=2)
self.short_term_intent_temp = self.gru_net_ins.time_prediction_gru_net(
hidden_units=self.num_units,
input_data=time_aware_gru_net_input,
input_length=tf.add(self.seq_length, -1), #
type='new',
scope='gru') # batch_size, max_len, num_units
short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=2 * self.num_units + 1,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1) #batch_size, num_units
self.interval_bar = short_term_intent[:,
-1] # 最后一位是interval batch_size,
short_term_intent = short_term_intent[:, :self.num_units] # 前面是h
# short_term_intent = tf.layers.dense(short_term_intent, self.num_units)
self.predict_behavior_emb = layer_norm(
short_term_intent) # batch_size, num_units
with tf.variable_scope('TimePredict', reuse=tf.AUTO_REUSE):
# TODO need modified
self.last_time = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=1,
sequence_tensor=self.time_list,
positions=self.mask_index - 1) # 上一个时间
self.interval = self.interval_bar
self.predict_time = tf.reshape(
tf.reshape(self.last_time, [
-1,
]) + tf.reshape(self.interval, [
-1,
]), [
-1,
])
# self.predict_time = self.target[2]
self.output()
def build_model(self):
time_aware_attention = Time_Aware_Attention()
self.gru_net_ins = GRU()
with tf.variable_scope("UserHistoryEncoder"):
user_history = time_aware_attention.self_attention(
enc=self.behavior_list_embedding_dense,
num_units=self.num_units,
num_heads=self.num_heads,
num_blocks=self.num_blocks,
dropout_rate=self.dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length,
t_querys=self.time_list,
t_keys=self.time_list,
t_keys_length=self.max_len,
t_querys_length=self.max_len)
with tf.variable_scope('ShortTermIntentEncoder'):
self.time_aware_gru_net_input = tf.concat([
self.behavior_list_embedding_dense,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(self.timenow_list, 2)
], 2)
self.short_term_intent_temp = self.gru_net_ins.time_aware_gru_net(
hidden_units=self.num_units,
input_data=self.time_aware_gru_net_input,
input_length=tf.add(self.seq_length, -1),
type='time-aware')
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = self.short_term_intent
short_term_intent4vallina = tf.expand_dims(self.short_term_intent,
1)
with tf.variable_scope('NextItemDecoder'):
hybird_preference = time_aware_attention.vanilla_attention(
user_history,
short_term_intent4vallina,
self.num_units,
self.num_heads,
self.num_blocks,
self.dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=tf.ones_like(short_term_intent4vallina[:, 0, 0],
dtype=tf.int32),
t_querys=tf.expand_dims(self.target[2], 1),
t_keys=self.time_list,
t_keys_length=self.max_len,
t_querys_length=1)
self.predict_behavior_emb = hybird_preference
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
with tf.variable_scope('OutputLayer'):
long_term_prefernce = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=user_history,
positions=self.mask_index)
self.long_term_prefernce = long_term_prefernce
self.short_term_intent = self.short_term_intent
self.hybird_preference = hybird_preference
self.z_concate = tf.concat([
self.long_term_prefernce, self.short_term_intent,
self.hybird_preference
], 1)
self.z = tf.layers.dense(
inputs=self.z_concate,
units=3,
kernel_regularizer=tf.contrib.layers.l2_regularizer(
self.regulation_rate))
self.z = tf.nn.softmax(self.z)
if self.FLAGS.PISTRec_type == 'hard':
if tf.argmax(self.z) == 0:
self.predict_behavior_emb = self.long_term_prefernce
elif tf.argmax(self.z) == 1:
self.predict_behavior_emb = self.short_term_intent
else:
self.predict_behavior_emb = self.hybird_preference
elif self.FLAGS.PISTRec_type == 'soft':
self.predict_behavior_emb = tf.multiply(tf.slice(self.z,[0,0],[-1,1]),self.long_term_prefernce)+\
tf.multiply(tf.slice(self.z, [0, 1], [-1, 1]), self.short_term_intent)+\
tf.multiply(tf.slice(self.z, [0, 2], [-1, 1]), self.hybird_preference)
elif self.FLAGS.PISTRec_type == 'short':
self.predict_behavior_emb = self.short_term_intent
elif self.FLAGS.PISTRec_type == 'long':
self.predict_behavior_emb = self.long_term_prefernce
elif self.FLAGS.PISTRec_type == 'hybird':
self.predict_behavior_emb = self.hybird_preference
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.output()
def build_model(self):
num_units = self.FLAGS.num_units
num_heads = self.FLAGS.num_heads
num_blocks = self.FLAGS.num_blocks
dropout_rate = self.FLAGS.dropout
time_aware_attention = Time_Aware_Attention()
self.gru_net_ins = GRU()
self.user_embedding, \
self.behavior_list_embedding_dense, \
self.item_list_emb,\
self.category_list_emb, \
self.position_list_emb,\
self.time_list, \
self.timelast_list, \
self.timenow_list, \
self.target, \
self.seq_length = self.embedding.get_embedding(num_units)
max_len = self.FLAGS.length_of_user_history
self.mask_index = tf.reshape(self.seq_length - 1,
[self.now_bacth_data_size, 1])
with tf.variable_scope("UserHistoryEncoder"):
user_history = time_aware_attention.self_attention(
enc=self.behavior_list_embedding_dense,
num_units=128,
num_heads=num_heads,
num_blocks=num_blocks,
dropout_rate=dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length,
t_querys=self.time_list,
t_keys=self.time_list,
t_keys_length=max_len,
t_querys_length=max_len)
self.user_h = user_history
with tf.variable_scope('ShortTermIntentEncoder'):
timelast_list = tf.expand_dims(self.timelast_list, 2)
self.time_aware_gru_net_input = tf.concat([
self.behavior_list_embedding_dense,
tf.expand_dims(self.timelast_list, 2),
tf.expand_dims(self.timenow_list, 2)
], 2)
self.error = tf.reshape(tf.add(self.seq_length, -1), [-1])
self.short_term_intent_temp = self.gru_net_ins.time_aware_gru_net(
hidden_units=num_units,
input_data=self.time_aware_gru_net_input,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
short_term_intent4vallina = tf.expand_dims(self.short_term_intent,
1)
with tf.variable_scope('NextItemDecoder'):
hybird_preference = time_aware_attention.vanilla_attention(
user_history,
short_term_intent4vallina,
num_units,
num_heads,
num_blocks,
dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=tf.ones_like(short_term_intent4vallina[:, 0, 0],
dtype=tf.int32),
t_querys=tf.expand_dims(self.target[2], 1),
t_keys=self.time_list,
t_keys_length=max_len,
t_querys_length=1)
#self.hybird_preference = hybird_preference
with tf.variable_scope('OutputLayer'):
long_term_prefernce = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_history,
positions=self.mask_index)
self.long_term_prefernce = long_term_prefernce
self.short_term_intent = self.short_term_intent
self.hybird_preference = hybird_preference
self.z_concate = tf.concat([
self.long_term_prefernce, self.short_term_intent,
self.hybird_preference
], 1)
self.z = tf.layers.dense(
inputs=self.z_concate,
units=3,
kernel_regularizer=tf.contrib.layers.l2_regularizer(
self.FLAGS.regulation_rate))
self.z = tf.nn.softmax(self.z)
if self.FLAGS.PISTRec_type == 'hard':
if tf.argmax(self.z) == 0:
self.predict_behavior_emb = self.long_term_prefernce
elif tf.argmax(self.z) == 1:
self.predict_behavior_emb = self.short_term_intent
else:
self.predict_behavior_emb = self.hybird_preference
elif self.FLAGS.PISTRec_type == 'soft':
self.predict_behavior_emb = tf.multiply(tf.slice(self.z,[0,0],[-1,1]),self.long_term_prefernce)+\
tf.multiply(tf.slice(self.z, [0, 1], [-1, 1]), self.short_term_intent)+\
tf.multiply(tf.slice(self.z, [0, 2], [-1, 1]), self.hybird_preference)
elif self.FLAGS.PISTRec_type == 'short':
self.predict_behavior_emb = self.short_term_intent
elif self.FLAGS.PISTRec_type == 'long':
self.predict_behavior_emb = self.long_term_prefernce
elif self.FLAGS.PISTRec_type == 'hybird':
self.predict_behavior_emb = self.hybird_preference
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
l2_norm = tf.add_n([
tf.nn.l2_loss(self.item_list_emb),
tf.nn.l2_loss(self.category_list_emb),
tf.nn.l2_loss(self.position_list_emb)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(
self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.target[0], [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=self.embedding.item_count + 3,
dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
pistloss = regulation_rate * l2_norm + tf.reduce_mean(
self.loss_origin)
with tf.name_scope('LearningtoRankLoss'):
self.loss = pistloss
tf.summary.scalar('l2_norm', l2_norm)
tf.summary.scalar('Training Loss', self.loss)
tf.summary.scalar('Learning_rate', self.learning_rate)
self.cal_gradient(tf.trainable_variables())
def build_model(self):
num_units = self.FLAGS.num_units
num_heads = self.FLAGS.num_heads
num_blocks = self.FLAGS.num_blocks
dropout_rate = self.FLAGS.dropout
embedding_size = self.FLAGS.itemid_embedding_size
use_mmoe = self.FLAGS.use_mmoe
attention_net = Attention()
gru_net_ins = GRU()
self.sequence_embedding, self.positive_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.mask_index, self.label_ids, \
self.seq_length, user_embedding, self.time_interval, \
self.time, self.pos_last_list = self.embedding.get_embedding(num_units)
with tf.variable_scope("LongTermIntentEncoder"):
attention_output = attention_net.self_attention(enc=self.behavior_embedding_result_dense,
num_units=128, num_heads=num_heads, num_blocks=num_blocks,
dropout_rate=dropout_rate, is_training=True, reuse=False,
key_length=self.seq_length, query_length=self.seq_length)
attention_pooling = tf.reduce_sum(attention_output, 1)
# flat_attention_output = tf.reshape(attention_output, shape=[-1, embedding_size])
# flat_behavior_emb = tf.reshape(self.behavior_embedding_result_dense, shape=[-1, embedding_size])
# concat_emb = tf.concat([flat_attention_output, flat_behavior_emb], axis=1)
# net = tf.layers.dense(concat_emb, embedding_size, activation=tf.nn.relu, use_bias=False)
# att_wgt = tf.layers.dense(net, 1, activation=tf.nn.relu, use_bias=False)
# att_wgt = tf.reshape(att_wgt, shape=[-1, seq_len])
# att_wgt = att_wgt / (embedding_size ** 0.5)
# att_wgt = tf.nn.softmax(att_wgt)
# att_wgt = tf.reshape(att_wgt, shape=[-1, seq_len, 1])
# output = tf.multiply(attention_output, att_wgt)
# attention_pooling = tf.reduce_sum(output, 1)
with tf.variable_scope("EnhanceUserPreferenceIntentEncoder"):
gru_input = tf.concat([self.behavior_embedding_result_dense,
tf.expand_dims(self.time_interval, 2),
tf.expand_dims(self.pos_last_list, 2)], 2)
self.gru_output = gru_net_ins.time_aware_gru_net(hidden_units=num_units,
input_data=gru_input,
input_length=tf.add(self.seq_length, -1))
self.gru_output = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=self.gru_output,
positions=tf.add(self.mask_index, -1))
_, seq_len, size = self.behavior_embedding_result_dense.get_shape().as_list()
feature_emb = tf.reshape(self.behavior_embedding_result_dense, [-1, seq_len * size])
concat_output = tf.concat([self.gru_output, attention_pooling, feature_emb], axis=1)
dence1 = tf.layers.dense(concat_output, concat_output.get_shape().as_list()[1] // 2, activation=tf.nn.relu, use_bias=False)
self.user_preference = tf.layers.dense(dence1, num_units, activation=tf.nn.relu, use_bias=False)
if use_mmoe:
print("mmoe")
with tf.variable_scope("mmoe"):
num_expert = 8
expert_outputs = []
for _ in range(num_expert):
expert_output = tf.layers.dense(self.user_preference, embedding_size, activation=tf.nn.relu, use_bias=False)
expert_output = tf.expand_dims(expert_output, axis=2) # [B, 64, 1]
expert_outputs.append(expert_output)
expert_outputs = tf.concat(expert_outputs, axis=2) # [B, 64, 8]
gate_network = tf.layers.dense(self.user_preference, num_expert, activation=tf.nn.softmax, use_bias=False)
gate_network_dim = tf.expand_dims(gate_network, axis=1) # [B, 1, 8]
weighted_expert_ouptputs = tf.tile(gate_network_dim, [1, embedding_size, 1]) * expert_outputs
final_output = tf.reduce_sum(weighted_expert_ouptputs, axis=2)
self.user_preference = tf.layers.dense(final_output, num_units, activation=tf.nn.relu, use_bias=False)
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = layer_norm(self.user_preference)
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding),
tf.nn.l2_loss(user_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(self.embedding.item_emb_lookup_table)
#print("item_embedding:", item_lookup_table_T.get_shape().as_list())
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids, depth=self.embedding.item_count+3, dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
if self.FLAGS.add_summary:
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(zip(clip_gradients, trainable_params))
self.summery()
def build_model(self):
num_units = self.FLAGS.num_units
# self.sequence_embedding, self.positive_embedding, self.negative_embedding, \
# self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
# self.negative_embedding_result_dense, self.mask_index, self.label_ids, \
# self.seq_length = self.embedding.get_embedding(num_units)
self.sequence_embedding, self.positive_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.mask_index, self.label_ids, \
self.seq_length, user_embedding, self.time = self.embedding.get_embedding(num_units)
gru = GRU()
gru_output = gru.bidirectional_gru_net(
num_units,
self.behavior_embedding_result_dense,
input_length=self.seq_length)
attention_net = Attention()
attention_output = attention_net.vanilla_attention_single(
queries=gru_output, keys=gru_output, keys_length=self.seq_length)
self.predict_behavior_emb = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=attention_output,
positions=self.mask_index)
# self.predict_behavior_emb = tf.reshape(attention_output, [self.now_bacth_data_size, num_units])
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
# self.mf_auc = tf.reduce_mean(
# tf.to_float((tf.reduce_sum(tf.multiply(tf.expand_dims(self.predict_behavior_emb, 1),
# tf.expand_dims(self.positive_embedding_result_dense,
# 1) - self.negative_embedding_result_dense), 2)) > 0))
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(
self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=self.embedding.item_count + 3,
dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(
self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(
gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(
zip(clip_gradients, trainable_params))
self.summery()
def build_model(self):
num_units = self.FLAGS.num_units
num_heads = self.FLAGS.num_heads
num_blocks = self.FLAGS.num_blocks
dropout_rate = self.FLAGS.dropout
attention_net = Attention()
# self.sequence_embedding, self.positive_embedding, self.negative_embedding, \
# self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
# self.negative_embedding_result_dense, self.mask_index, self.label_ids, \
# self.seq_length = self.embedding.get_embedding(num_units)
self.sequence_embedding, self.positive_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.mask_index, self.label_ids, \
self.seq_length, user_embedding, self.time = self.embedding.get_embedding(num_units)
with tf.variable_scope('ShortTermIntentEncoder'):
long_term_intent_temp = attention_net.self_attention_single(
enc=self.behavior_embedding_result_dense,
num_units=128,
num_heads=num_heads,
num_blocks=num_blocks,
dropout_rate=dropout_rate,
is_training=True,
reuse=False)
long_term_intent_dense = tf.layers.dense(
long_term_intent_temp,
num_units,
activation=tf.nn.relu,
use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-5),
name='mlp',
reuse=False)
self.long_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=long_term_intent_dense,
positions=self.mask_index)
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = self.long_term_intent
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
# self.mf_auc = tf.reduce_mean(tf.to_float((tf.reduce_sum(tf.multiply(tf.expand_dims(self.predict_behavior_emb, 1),
# tf.expand_dims(self.positive_embedding_result_dense, 1) - self.negative_embedding_result_dense), 2)) > 0))
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(
self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=self.embedding.item_count + 3,
dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(
self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(
gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(
zip(clip_gradients, trainable_params))
self.summery()
def build_model(self):
num_units = self.FLAGS.num_units
num_heads = self.FLAGS.num_heads
num_blocks = self.FLAGS.num_blocks
dropout_rate = self.FLAGS.dropout
attention_net = Attention()
gru_net_ins = GRU()
self.sequence_embedding, self.positive_embedding, self.negative_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.negative_embedding_result_dense, self.mask_index, self.label_ids, \
self.seq_length, user_embedding, time = self.embedding.get_embedding(num_units)
with tf.variable_scope("LongTermIntentEncoder"):
long_term_intent_temp = attention_net.self_attention(
enc=self.behavior_embedding_result_dense,
num_units=128,
num_heads=num_heads,
num_blocks=num_blocks,
dropout_rate=dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length)
self.long_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=long_term_intent_temp,
positions=self.mask_index)
# average pooling for long_term_intent_temp
self.long_term_preference = tf.reduce_mean(long_term_intent_temp,
axis=1)
with tf.variable_scope('ShortTermIntentEncoder'):
short_term_intent_temp = gru_net_ins.gru_net(
hidden_units=num_units,
input_data=self.behavior_embedding_result_dense,
input_length=self.mask_index)
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=short_term_intent_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("EnhancePreferenceIntentEncoder"):
user_enhance_preference_temp = gru_net_ins.gru_net_initial(
hidden_units=num_units,
input_length=self.mask_index,
input_data=long_term_intent_temp,
initial_state=self.long_term_preference)
self.user_enhance_preference = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_enhance_preference_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("EnhanceUserPreferenceIntentEncoder"):
user_enhance_preference_temp_user = gru_net_ins.gru_net_initial(
hidden_units=num_units,
input_length=self.mask_index,
input_data=long_term_intent_temp,
initial_state=user_embedding)
self.user_enhance_preference_user = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_enhance_preference_temp_user,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("PreferenceEncoder"):
user_preference_temp = gru_net_ins.gru_net(
hidden_units=num_units,
input_data=long_term_intent_temp,
input_length=self.mask_index)
self.user_preference = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_preference_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = self.user_enhance_preference_user
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.mf_auc = tf.reduce_mean(
tf.to_float((tf.reduce_sum(
tf.multiply(
tf.expand_dims(self.predict_behavior_emb, 1),
tf.expand_dims(self.positive_embedding_result_dense, 1)
- self.negative_embedding_result_dense), 2)) > 0))
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding),
tf.nn.l2_loss(self.negative_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(
self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=500000,
dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(
self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(
gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(
zip(clip_gradients, trainable_params))
self.summery()