"""Implementation of DIN."""
from ..bases import ModelMeta, TfBase
from ..batch.sequence import get_recent_seqs
from ..layers import dense_nn, din_attention, embedding_lookup, tf_attention, tf_dense
from ..tfops import dropout_config, reg_config, tf
from ..tfops.features import (
combine_seq_features,
compute_dense_feats,
compute_sparse_feats,
)
from ..torchops import hidden_units_config
from ..utils.misc import count_params
from ..utils.validate import (
check_dense_values,
check_multi_sparse,
check_seq_mode,
check_sparse_indices,
dense_field_size,
sparse_feat_size,
sparse_field_size,
)
[docs]class DIN(TfBase, metaclass=ModelMeta):
"""*Deep Interest Network* algorithm.
Parameters
----------
task : {'rating', 'ranking'}
Recommendation task. See :ref:`Task`.
data_info : :class:`~libreco.data.DataInfo` object
Object that contains useful information for training and inference.
loss_type : {'cross_entropy', 'focal'}, default: 'cross_entropy'
Loss for model training.
embed_size: int, default: 16
Vector size of embeddings.
n_epochs: int, default: 10
Number of epochs for training.
lr : float, default 0.001
Learning rate for training.
lr_decay : bool, default: False
Whether to use learning rate decay.
epsilon : float, default: 1e-5
A small constant added to the denominator to improve numerical stability in
Adam optimizer.
According to the `official comment <https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/adam.py#L64>`_,
default value of `1e-8` for `epsilon` is generally not good, so here we choose `1e-5`.
Users can try tuning this hyperparameter if the training is unstable.
reg : float or None, default: None
Regularization parameter, must be non-negative or None.
batch_size : int, default: 256
Batch size for training.
sampler : {'random', 'unconsumed', 'popular'}, default: 'random'
Negative sampling strategy.
- ``'random'`` means random sampling.
- ``'unconsumed'`` samples items that the target user did not consume before.
- ``'popular'`` has a higher probability to sample popular items as negative samples.
.. versionadded:: 1.1.0
num_neg : int, default: 1
Number of negative samples for each positive sample, only used in `ranking` task.
use_bn : bool, default: True
Whether to use batch normalization.
dropout_rate : float or None, default: None
Probability of an element to be zeroed. If it is None, dropout is not used.
hidden_units : int, list of int or tuple of (int,), default: (128, 64, 32)
Number of layers and corresponding layer size in MLP.
.. versionchanged:: 1.0.0
Accept type of ``int``, ``list`` or ``tuple``, instead of ``str``.
recent_num : int or None, default: 10
Number of recent items to use in user behavior sequence.
random_num : int or None, default: None
Number of random sampled items to use in user behavior sequence.
If `recent_num` is not None, `random_num` is not considered.
use_tf_attention : bool, default: False
Whether to use TensorFlow's `attention <https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/Attention>`_ implementation.
The TensorFlow attention version is simpler and faster, but doesn't follow the
settings in paper, whereas our implementation does.
multi_sparse_combiner : {'normal', 'mean', 'sum', 'sqrtn'}, default: 'sqrtn'
Options for combining `multi_sparse` features.
seed : int, default: 42
Random seed.
lower_upper_bound : tuple or None, default: None
Lower and upper score bound for `rating` task.
tf_sess_config : dict or None, default: None
Optional TensorFlow session config, see `ConfigProto options
<https://github.com/tensorflow/tensorflow/blob/v2.10.0/tensorflow/core/protobuf/config.proto#L431>`_.
References
----------
*Guorui Zhou et al.* `Deep Interest Network for Click-Through Rate Prediction
<https://arxiv.org/pdf/1706.06978.pdf>`_.
"""
user_variables = ("embedding/user_embeds_var",)
item_variables = ("embedding/item_embeds_var",)
sparse_variables = ("embedding/sparse_embeds_var",)
dense_variables = ("embedding/dense_embeds_var",)
def __init__(
self,
task,
data_info=None,
loss_type="cross_entropy",
embed_size=16,
n_epochs=20,
lr=0.001,
lr_decay=False,
epsilon=1e-5,
reg=None,
batch_size=256,
sampler="random",
num_neg=1,
use_bn=True,
dropout_rate=None,
hidden_units=(128, 64, 32),
recent_num=10,
random_num=None,
use_tf_attention=False,
multi_sparse_combiner="sqrtn",
seed=42,
lower_upper_bound=None,
tf_sess_config=None,
):
super().__init__(task, data_info, lower_upper_bound, tf_sess_config)
self.all_args = locals()
self.loss_type = loss_type
self.embed_size = embed_size
self.n_epochs = n_epochs
self.lr = lr
self.lr_decay = lr_decay
self.epsilon = epsilon
self.reg = reg_config(reg)
self.batch_size = batch_size
self.sampler = sampler
self.num_neg = num_neg
self.use_bn = use_bn
self.dropout_rate = dropout_config(dropout_rate)
self.hidden_units = hidden_units_config(hidden_units)
self.use_tf_attention = use_tf_attention
self.seq_mode, self.max_seq_len = check_seq_mode(recent_num, random_num)
self.recent_seqs, self.recent_seq_lens = get_recent_seqs(
self.n_users,
self.user_consumed,
self.n_items,
self.max_seq_len,
)
self.seed = seed
self.sparse = check_sparse_indices(data_info)
self.dense = check_dense_values(data_info)
if self.sparse:
self.sparse_feature_size = sparse_feat_size(data_info)
self.sparse_field_size = sparse_field_size(data_info)
self.multi_sparse_combiner = check_multi_sparse(
data_info, multi_sparse_combiner
)
if self.dense:
self.dense_field_size = dense_field_size(data_info)
def build_model(self):
tf.set_random_seed(self.seed)
self._build_placeholders()
user_embed = embedding_lookup(
indices=self.user_indices,
var_name="user_embeds_var",
var_shape=(self.n_users + 1, self.embed_size),
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
item_embed = embedding_lookup(
indices=self.item_indices,
var_name="item_embeds_var",
var_shape=(self.n_items + 1, self.embed_size),
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
concat_embeds = [user_embed, item_embed]
if self.sparse:
sparse_embed = compute_sparse_feats(
self.data_info,
self.multi_sparse_combiner,
self.sparse_indices,
var_name="sparse_embeds_var",
var_shape=(self.sparse_feature_size, self.embed_size),
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
flatten=True,
)
concat_embeds.append(sparse_embed)
if self.dense:
dense_embed = compute_dense_feats(
self.dense_values,
var_name="dense_embeds_var",
var_shape=(self.dense_field_size, self.embed_size),
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
flatten=True,
)
concat_embeds.append(dense_embed)
item_seq_feats = combine_seq_features(self.data_info, feat_agg_mode="concat")
attention_embeds = self._build_seq_attention(item_seq_feats)
dense_inputs = tf.concat([*concat_embeds, attention_embeds], axis=1)
mlp_layer = dense_nn(
dense_inputs,
self.hidden_units,
use_bn=self.use_bn,
dropout_rate=self.dropout_rate,
is_training=self.is_training,
name="mlp",
)
self.output = tf.reshape(tf_dense(units=1)(mlp_layer), [-1])
self.serving_topk = self.build_topk(self.output)
count_params()
def _build_placeholders(self):
self.user_indices = tf.placeholder(tf.int32, shape=[None])
self.item_indices = tf.placeholder(tf.int32, shape=[None])
self.user_interacted_seq = tf.placeholder(
tf.int32, shape=[None, self.max_seq_len]
)
self.user_interacted_len = tf.placeholder(tf.int32, shape=[None])
self.labels = tf.placeholder(tf.float32, shape=[None])
self.is_training = tf.placeholder_with_default(False, shape=[])
if self.sparse:
self.sparse_indices = tf.placeholder(
tf.int32, shape=[None, self.sparse_field_size]
)
if self.dense:
self.dense_values = tf.placeholder(
tf.float32, shape=[None, self.dense_field_size]
)
def _build_seq_attention(self, item_seq_feats):
# B * K
item_embeds = tf.nn.embedding_lookup(item_seq_feats, self.item_indices)
# B * seq * K
seq_embeds = tf.nn.embedding_lookup(item_seq_feats, self.user_interacted_seq)
seq_mask = tf.sequence_mask(self.user_interacted_len, self.max_seq_len)
if self.use_tf_attention:
return tf_attention(item_embeds, seq_embeds, seq_mask)
else:
return din_attention(item_embeds, seq_embeds, seq_mask)