def _build_newsencoder(self, embedding_layer):
"""The main function to create news encoder of LSTUR.
Args:
embedding_layer(obj): a word embedding layer.
Return:
obj: the news encoder of LSTUR.
"""
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams.doc_size, ),
dtype="int32")
embedded_sequences_title = embedding_layer(sequences_input_title)
y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
y = layers.Conv1D(
hparams.filter_num,
hparams.window_size,
activation=hparams.cnn_activation,
padding="same",
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
)(y)
y = layers.Dropout(hparams.dropout)(y)
y = layers.Masking()(
OverwriteMasking()([y, ComputeMasking()(sequences_input_title)]))
pred_title = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
model = keras.Model(sequences_input_title,
pred_title,
name="news_encoder")
return model
def _build_newsencoder(self, embedding_layer):
hparams = self.hparams
input_title_body_verts = keras.Input(
shape=(hparams.title_size + hparams.body_size + 2,), dtype="int32"
)
#按照不同的类型建不同的层
sequences_input_title = layers.Lambda(lambda x: x[:, : hparams.title_size])(
input_title_body_verts
)
sequences_input_body = layers.Lambda(
lambda x: x[:, hparams.title_size : hparams.title_size + hparams.body_size]
)(input_title_body_verts)
#对各种类型进行encoder
title_repr = self._build_titleencoder(embedding_layer)(sequences_input_title)
body_repr = self._build_bodyencoder(embedding_layer)(sequences_input_body)
#连接
concate_repr = layers.Concatenate(axis=-2)(
[title_repr, body_repr]
)
#attention机制
news_repr = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(
concate_repr
)
model = keras.Model(input_title_body_verts, news_repr, name="news_encoder")
return model
def _build_bodyencoder(self, embedding_layer):
"""build body encoder of NAML news encoder.
Args:
embedding_layer(obj): a word embedding layer.
Return:
obj: the body encoder of NAML.
"""
hparams = self.hparams
sequences_input_body = keras.Input(shape=(hparams.body_size,), dtype="int32")
embedded_sequences_body = embedding_layer(sequences_input_body)
y = layers.Dropout(hparams.dropout)(embedded_sequences_body)
y = layers.Conv1D(
hparams.filter_num,
hparams.window_size,
activation=hparams.cnn_activation,
padding="same",
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(seed=self.seed),
)(y)
y = layers.Dropout(hparams.dropout)(y)
pred_body = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
pred_body = layers.Reshape((1, hparams.filter_num))(pred_body)
model = keras.Model(sequences_input_body, pred_body, name="body_encoder")
return model
def _build_userencoder(self, newsencoder):
"""The main function to create user encoder of NAML.
Args:
newsencoder(obj): the news encoder of NAML.
Return:
obj: the user encoder of NAML.
"""
hparams = self.hparams
his_input_title_body_verts = keras.Input(
shape=(hparams.his_size, hparams.title_size + hparams.body_size + 2),
dtype="int32",
)
click_news_presents = layers.TimeDistributed(newsencoder)(
his_input_title_body_verts
)
user_present = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(
click_news_presents
)
model = keras.Model(
his_input_title_body_verts, user_present, name="user_encoder"
)
return model
def _build_newsencoder(self, embedding_layer):
"""The main function to create news encoder of NRMS.
Args:
embedding_layer(obj): a word embedding layer.
Return:
obj: the news encoder of NRMS.
"""
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams.title_size, ),
dtype="int32")
embedded_sequences_title = embedding_layer(sequences_input_title)
y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
y = SelfAttention(hparams.head_num, hparams.head_dim,
seed=self.seed)([y, y, y])
y = layers.Dropout(hparams.dropout)(y)
pred_title = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
model = keras.Model(sequences_input_title,
pred_title,
name="news_encoder")
return model
def _build_userencoder(self, titleencoder, entityencoder, contextencoder):
"""The main function to create user encoder of NRMS.
Args:
titleencoder(obj): the news encoder of NRMS.
Return:
obj: the user encoder of NRMS.
"""
hparams = self.hparams
his_input_title = keras.Input(shape=(hparams.his_size,
hparams.title_size),
dtype="int32")
click_title_presents = layers.TimeDistributed(
titleencoder, name='news_time_distributed')(his_input_title)
y = SelfAttention(hparams.head_num, hparams.head_dim,
seed=self.seed)([click_title_presents] * 3)
if entityencoder is not None:
his_input_title_entity = keras.Input(shape=(hparams.his_size,
hparams.title_size),
dtype="int32")
click_title_entity_presents = layers.TimeDistributed(
entityencoder,
name='entity_time_distributed')(his_input_title_entity)
entity_y = SelfAttention(hparams.head_num,
hparams.head_dim,
seed=self.seed)(
[click_title_entity_presents] * 3)
if contextencoder is not None:
click_title_context_presents = layers.TimeDistributed(
contextencoder,
name='context_time_distributed')(his_input_title_entity)
context_y = SelfAttention(
hparams.head_num, hparams.head_dim,
seed=self.seed)([click_title_context_presents] * 3)
y = layers.Concatenate()([y, entity_y, context_y])
else:
y = layers.Concatenate()([y, entity_y])
user_present = AttLayer2(hparams.attention_hidden_dim,
seed=self.seed)(y)
if entityencoder is not None:
model = keras.Model(
inputs=[his_input_title, his_input_title_entity],
outputs=user_present,
name="user_encoder")
else:
model = keras.Model(his_input_title,
user_present,
name="user_encoder")
return model
def _build_newsencoder(self, embedding_layer):
"""The main function to create news encoder of NAML.
news encoder in composed of title encoder, body encoder, vert encoder and subvert encoder
Args:
embedding_layer(obj): a word embedding layer.
Return:
obj: the news encoder of NAML.
"""
hparams = self.hparams
input_title_body_verts = keras.Input(shape=(hparams.title_size +
hparams.body_size + 2, ),
dtype="int32")
sequences_input_title = layers.Lambda(
lambda x: x[:, :hparams.title_size])(input_title_body_verts)
sequences_input_body = layers.Lambda(
lambda x: x[:, hparams.title_size:hparams.title_size + hparams.
body_size])(input_title_body_verts)
input_vert = layers.Lambda(
lambda x: x[:, hparams.title_size + hparams.body_size:hparams.
title_size + hparams.body_size + 1, ])(
input_title_body_verts)
input_subvert = layers.Lambda(
lambda x: x[:, hparams.title_size + hparams.body_size + 1:])(
input_title_body_verts)
title_repr = self._build_titleencoder(embedding_layer)(
sequences_input_title)
body_repr = self._build_bodyencoder(embedding_layer)(
sequences_input_body)
vert_repr = self._build_vertencoder()(input_vert)
subvert_repr = self._build_subvertencoder()(input_subvert)
concate_repr = layers.Concatenate(axis=-2)(
[title_repr, body_repr, vert_repr, subvert_repr])
# multi-head attention module.
y = layers.Dropout(hparams.dropout)(concate_repr)
y = SelfAttention(hparams.head_num, hparams.head_dim,
seed=self.seed)([y, y, y])
y = layers.Dropout(hparams.dropout)(y)
news_repr = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
model = keras.Model(input_title_body_verts,
news_repr,
name="news_encoder")
return model
def _build_titleencoder(self, embedding_layer):
#和lstur的新闻编码器相同参数不同
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams.title_size,), dtype="int32")
embedded_sequences_title = embedding_layer(sequences_input_title)
y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
y = layers.Conv1D(
hparams.filter_num,
hparams.window_size,
activation=hparams.cnn_activation,
padding="same",
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(seed=self.seed),
)(y)
y = layers.Dropout(hparams.dropout)(y)
pred_title = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
pred_title = layers.Reshape((1, hparams.filter_num))(pred_title)
model = keras.Model(sequences_input_title, pred_title, name="title_encoder")
return model
def _build_userencoder(self, titleencoder):
"""The main function to create user encoder of NRMS.
Args:
titleencoder(obj): the news encoder of NRMS.
Return:
obj: the user encoder of NRMS.
"""
hparams = self.hparams
his_input_title = keras.Input(
shape=(hparams.his_size, hparams.title_size), dtype="int32"
)
click_title_presents = layers.TimeDistributed(titleencoder)(his_input_title)
y = SelfAttention(hparams.head_num, hparams.head_dim, seed=self.seed)(
[click_title_presents] * 3
)
user_present = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
model = keras.Model(his_input_title, user_present, name="user_encoder")
return model
def _build_bodyencoder(self, embedding_layer):
hparams = self.hparams
sequences_input_body = keras.Input(shape=(hparams.body_size,), dtype="int32")
embedded_sequences_body = embedding_layer(sequences_input_body)
y = layers.Dropout(hparams.dropout)(embedded_sequences_body)
y = layers.Conv1D(
hparams.filter_num,
hparams.window_size,
activation=hparams.cnn_activation,
padding="same",
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(seed=self.seed),
)(y)
y = layers.Dropout(hparams.dropout)(y)
pred_body = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
pred_body = layers.Reshape((1, hparams.filter_num))(pred_body)
model = keras.Model(sequences_input_body, pred_body, name="body_encoder")
return model
def _build_lsturnaml(self):
hparams = self.hparams
his_input_title = keras.Input(
shape=(hparams.his_size, hparams.title_size), dtype="int32"
)
his_input_body = keras.Input(
shape=(hparams.his_size, hparams.body_size), dtype="int32"
)
pred_input_title = keras.Input(
shape=(hparams.npratio + 1, hparams.title_size), dtype="int32"
)
pred_input_body = keras.Input(
shape=(hparams.npratio + 1, hparams.body_size), dtype="int32"
)
pred_input_title_one = keras.Input(
shape=(1, hparams.title_size,), dtype="int32"
)
pred_input_body_one = keras.Input(shape=(1, hparams.body_size,), dtype="int32")
his_title_body = layers.Concatenate(axis=-1)(
[his_input_title, his_input_body]
)
pred_title_body = layers.Concatenate(axis=-1)(
[pred_input_title, pred_input_body]
)
pred_title_body_one = layers.Concatenate(axis=-1)(
[
pred_input_title_one,
pred_input_body_one,
]
)
pred_title_body_one = layers.Reshape((-1,))(pred_title_body_one)
embedding_layer = layers.Embedding(
self.word2vec_embedding.shape[0],
hparams.word_emb_dim,
weights=[self.word2vec_embedding],
trainable=True,
)
self.newsencoder = self._build_newsencoder(embedding_layer)
self.userencoder = self._build_userencoder(self.newsencoder)
user_present = self.userencoder(his_title_body)
news_present = layers.TimeDistributed(self.newsencoder)(pred_title_body)
news_present_one = self.newsencoder(pred_title_body_one)
preds = layers.Dot(axes=-1)([news_present, user_present])
preds = layers.Activation(activation="softmax")(preds)
pred_one = layers.Dot(axes=-1)([news_present_one, user_present])
pred_one = layers.Activation(activation="sigmoid")(pred_one)
model = keras.Model(
[
his_input_title,
his_input_body,
pred_input_title,
pred_input_body,
],
preds,
)
scorer = keras.Model(
[
his_input_title,
his_input_body,
pred_input_title_one,
pred_input_body_one,
],
pred_one,
)
return model, scorer
def _build_userencoder(self, titleencoder, type="ini"):
"""The main function to create user encoder of LSTUR.
Args:
titleencoder(obj): the news encoder of LSTUR.
Return:
obj: the user encoder of LSTUR.
"""
hparams = self.hparams
his_input_title = keras.Input(shape=(hparams.his_size,
hparams.doc_size),
dtype="int32")
user_indexes = keras.Input(shape=(1, ), dtype="int32")
user_embedding_layer = layers.Embedding(
hparams.user_num,
hparams.gru_unit,
trainable=True,
embeddings_initializer="zeros",
)
long_u_emb = layers.Reshape(
(hparams.gru_unit, ))(user_embedding_layer(user_indexes))
click_title_presents = layers.TimeDistributed(titleencoder)(
his_input_title)
if type == "ini":
user_present = layers.GRU(
hparams.gru_unit,
kernel_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
recurrent_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
bias_initializer=keras.initializers.Zeros(),
)(
layers.Masking(mask_value=0.0)(click_title_presents),
initial_state=[long_u_emb],
)
elif type == "con":
short_uemb = layers.GRU(
hparams.gru_unit,
kernel_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
recurrent_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
bias_initializer=keras.initializers.Zeros(),
)(layers.Masking(mask_value=0.0)(click_title_presents))
user_present = layers.Concatenate()([short_uemb, long_u_emb])
user_present = layers.Dense(
hparams.gru_unit,
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
)(user_present)
click_title_presents = layers.TimeDistributed(titleencoder)(
his_input_title)
user_present = AttLayer2(hparams.attention_hidden_dim,
seed=self.seed)(click_title_presents)
model = keras.Model([his_input_title, user_indexes],
user_present,
name="user_encoder")
return model