# Copyright (c) Recommenders contributors.
# Licensed under the MIT License.
import tensorflow.keras as keras
from tensorflow.keras import layers
from recommenders.models.newsrec.models.base_model import BaseModel
from recommenders.models.newsrec.models.layers import AttLayer2, SelfAttention
__all__ = ["NRMSModel"]
[docs]class NRMSModel(BaseModel):
"""NRMS model(Neural News Recommendation with Multi-Head Self-Attention)
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang,and Xing Xie, "Neural News
Recommendation with Multi-Head Self-Attention" in Proceedings of the 2019 Conference
on Empirical Methods in Natural Language Processing and the 9th International Joint Conference
on Natural Language Processing (EMNLP-IJCNLP)
Attributes:
word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
hparam (object): Global hyper-parameters.
"""
[docs] def __init__(
self,
hparams,
iterator_creator,
seed=None,
):
"""Initialization steps for NRMS.
Compared with the BaseModel, NRMS need word embedding.
After creating word embedding matrix, BaseModel's __init__ method will be called.
Args:
hparams (object): Global hyper-parameters. Some key setttings such as head_num and head_dim are there.
iterator_creator_train (object): NRMS data loader class for train data.
iterator_creator_test (object): NRMS data loader class for test and validation data
"""
self.word2vec_embedding = self._init_embedding(hparams.wordEmb_file)
super().__init__(
hparams,
iterator_creator,
seed=seed,
)
def _get_input_label_from_iter(self, batch_data):
"""get input and labels for trainning from iterator
Args:
batch data: input batch data from iterator
Returns:
list: input feature fed into model (clicked_title_batch & candidate_title_batch)
numpy.ndarray: labels
"""
input_feat = [
batch_data["clicked_title_batch"],
batch_data["candidate_title_batch"],
]
input_label = batch_data["labels"]
return input_feat, input_label
def _get_user_feature_from_iter(self, batch_data):
"""get input of user encoder
Args:
batch_data: input batch data from user iterator
Returns:
numpy.ndarray: input user feature (clicked title batch)
"""
return batch_data["clicked_title_batch"]
def _get_news_feature_from_iter(self, batch_data):
"""get input of news encoder
Args:
batch_data: input batch data from news iterator
Returns:
numpy.ndarray: input news feature (candidate title batch)
"""
return batch_data["candidate_title_batch"]
def _build_graph(self):
"""Build NRMS model and scorer.
Returns:
object: a model used to train.
object: a model used to evaluate and inference.
"""
model, scorer = self._build_nrms()
return model, scorer
def _build_userencoder(self, titleencoder):
"""The main function to create user encoder of NRMS.
Args:
titleencoder (object): the news encoder of NRMS.
Return:
object: 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_newsencoder(self, embedding_layer):
"""The main function to create news encoder of NRMS.
Args:
embedding_layer (object): a word embedding layer.
Return:
object: 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_nrms(self):
"""The main function to create NRMS's logic. The core of NRMS
is a user encoder and a news encoder.
Returns:
object: a model used to train.
object: a model used to evaluate and inference.
"""
hparams = self.hparams
his_input_title = keras.Input(
shape=(hparams.his_size, hparams.title_size), dtype="int32"
)
pred_input_title = keras.Input(
shape=(hparams.npratio + 1, hparams.title_size), dtype="int32"
)
pred_input_title_one = keras.Input(
shape=(
1,
hparams.title_size,
),
dtype="int32",
)
pred_title_one_reshape = layers.Reshape((hparams.title_size,))(
pred_input_title_one
)
embedding_layer = layers.Embedding(
self.word2vec_embedding.shape[0],
hparams.word_emb_dim,
weights=[self.word2vec_embedding],
trainable=True,
)
titleencoder = self._build_newsencoder(embedding_layer)
self.userencoder = self._build_userencoder(titleencoder)
self.newsencoder = titleencoder
user_present = self.userencoder(his_input_title)
news_present = layers.TimeDistributed(self.newsencoder)(pred_input_title)
news_present_one = self.newsencoder(pred_title_one_reshape)
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, pred_input_title], preds)
scorer = keras.Model([his_input_title, pred_input_title_one], pred_one)
return model, scorer