def build_model(self):
attention = Attention()
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'):
self.short_term_intent_temp = self.ggnn_model.generate_time_aware_emb(
init_emb=user_behavior_list_embedding,
adj_avg_time=self.adj_avg_time,
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)
# 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):
attention = Attention()
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',reuse=tf.AUTO_REUSE):
self.gnn_emb_vec = 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=self.FLAGS.graph_step)
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.gnn_emb_vec,
positions=self.mask_index - 1) # batch_size, num_units
with tf.variable_scope('self_attention',reuse=tf.AUTO_REUSE):
self.att_emb_vec = attention.self_attention(enc = self.gnn_emb_vec,
num_units = self.num_units,
num_heads = self.num_heads,
num_blocks = self.num_blocks,
dropout_rate = self.dropout_rate,
is_training = True,
reuse = None,
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.max_len,
width=self.num_units,
sequence_tensor=self.att_emb_vec,
positions=self.mask_index ) # batch_size, num_units
with tf.variable_scope('sess_emb', reuse=tf.AUTO_REUSE):
eps = tf.get_variable('eps',[1],dtype=tf.float32)
self.predict_behavior_emb = eps*self.short_term_intent +(1-eps) * self.long_term_intent
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)
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 = GraphRNN()
self.gated_gnn_model = gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
for i in range(2):
with tf.variable_scope('neighbor_emb_' + str(i),
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_' + str(i),
reuse=tf.AUTO_REUSE):
grnn_inputs = tf.concat(
[user_behavior_list_embedding, structure_emb], axis=2)
user_behavior_list_embedding = 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=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):
self.gru_net_ins = GraphRNN()
self.gated_gnn_model = 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_adj_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,
) # 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 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()