def _build_item_extractions(self, train=True):
self._add_module(
"item_vec",
LatentFactor(
l2_reg=self._l2_reg,
init="normal",
ids=self._get_input("item_id", train=train),
shape=[self._max_item, self._dim_embed],
scope="item",
reuse=not train,
),
train=train,
)
self._add_module(
"item_bias",
LatentFactor(
l2_reg=self._l2_reg,
init="zero",
ids=self._get_input("item_id", train=train),
shape=[self._max_item, 1],
scope="item_bias",
reuse=not train,
),
train=train,
)
def _build_item_extractions(self, train=True):
self._add_module(
"item_vec",
TemporalLatentFactor(
l2_reg=self._l2_reg,
init=self._pretrained_item_embeddings,
ids=self._get_input("item_id", train=train),
mlp_dims=self._item_dims,
shape=[self._max_item, self._dim_embed],
train=train,
scope="item",
reuse=not train,
),
train=train,
)
self._add_module(
"item_bias",
LatentFactor(
l2_reg=self._l2_reg,
init="zero",
ids=self._get_input("item_id", train=train),
shape=[self._max_item, 1],
scope="item_bias",
reuse=not train,
),
train=train,
)
def _build_user_extractions(self, train=True):
self._add_module('user_vec',
LatentFactor(l2_reg=self._l2_reg,
init='normal',
ids=self._get_input('user_id',
train=train),
shape=[self._max_user, self._dim_embed],
scope='user',
reuse=not train),
train=train)
def _build_item_extractions(self, train=True):
self._add_module('item_vec',
LatentFactor(l2_reg=self._l2_reg,
init='normal',
ids=self._get_input('item_id',
train=train),
shape=[self._max_item, self._dim_embed],
scope='item',
reuse=not train),
train=train)
self._add_module('item_bias',
LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._get_input('item_id',
train=train),
shape=[self._max_item, 1],
scope='item_bias',
reuse=not train),
train=train)
def _build_user_extractions(self, train=True):
self._add_module(
"user_vec",
LatentFactor(
l2_reg=self._l2_reg,
init="normal",
ids=self._get_input("user_id", train=train),
shape=[self._max_user, self._dim_embed],
scope="user",
reuse=(not train),
),
train=train,
)
def _build_item_extractions(self, train=True):
self._add_module('item_vec',
TemporalLatentFactor(
l2_reg=self._l2_reg,
init=self._pretrained_item_embeddings,
ids=self._get_input('item_id', train=train),
mlp_dims=self._item_dims,
shape=[self._max_item, self._dim_embed],
train=train,
scope='item',
reuse=not train),
train=train)
self._add_module('item_bias',
LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._get_input('item_id',
train=train),
shape=[self._max_item, 1],
scope='item_bias',
reuse=not train),
train=train)
def _build_item_extractions(self, train=True):
if train:
self._add_module(
'p_item_lf',
LatentFactor(
init='normal',
l2_reg=self._l2_reg,
ids=self._get_input('p_item_id'),
shape=[self._max_item, self._dim_embed - self._dim_ve],
scope='item',
reuse=False))
self._add_module(
'p_item_vf',
MultiLayerFC(in_tensor=self._get_input('p_item_vfeature'),
dims=[self._dim_ve],
scope='item_MLP',
reuse=False))
self._add_module(
'p_item_bias',
LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._get_input('p_item_id'),
shape=[self._max_item, 1],
scope='item_bias',
reuse=False))
self._add_module(
'n_item_lf',
LatentFactor(
init='normal',
l2_reg=self._l2_reg,
ids=self._get_input('n_item_id'),
shape=[self._max_item, self._dim_embed - self._dim_ve],
scope='item',
reuse=True))
self._add_module(
'n_item_vf',
MultiLayerFC(in_tensor=self._get_input('n_item_vfeature'),
dims=[self._dim_ve],
scope='item_MLP',
reuse=True))
self._add_module(
'n_item_bias',
LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._get_input('n_item_id'),
shape=[self._max_item, 1],
scope='item_bias',
reuse=True))
else:
self._add_module('item_lf',
LatentFactor(init='normal',
l2_reg=self._l2_reg,
ids=self._get_input('item_id',
train=train),
shape=[
self._max_item,
self._dim_embed - self._dim_ve
],
scope='item',
reuse=True),
train=False)
self._add_module('item_vf',
MultiLayerFC(in_tensor=self._get_input(
'item_vfeature', train=train),
dims=[self._dim_ve],
scope='item_MLP',
reuse=True),
train=False)
self._add_module('item_bias',
LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._get_input('item_id',
train=train),
shape=[self._max_item, 1],
scope='item_bias',
reuse=True),
train=False)
def _build_item_extractions(self, train=True):
if train:
self._add_module(
"p_item_lf",
LatentFactor(
init="normal",
l2_reg=self._l2_reg,
ids=self._get_input("p_item_id"),
shape=[self._max_item, self._dim_embed - self._dim_ve],
scope="item",
reuse=False,
),
)
self._add_module(
"p_item_vf",
MultiLayerFC(
in_tensor=self._get_input("p_item_vfeature"),
dims=[self._dim_ve],
scope="item_MLP",
reuse=False,
),
)
self._add_module(
"p_item_bias",
LatentFactor(
l2_reg=self._l2_reg,
init="zero",
ids=self._get_input("p_item_id"),
shape=[self._max_item, 1],
scope="item_bias",
reuse=False,
),
)
self._add_module(
"n_item_lf",
LatentFactor(
init="normal",
l2_reg=self._l2_reg,
ids=self._get_input("n_item_id"),
shape=[self._max_item, self._dim_embed - self._dim_ve],
scope="item",
reuse=True,
),
)
self._add_module(
"n_item_vf",
MultiLayerFC(
in_tensor=self._get_input("n_item_vfeature"),
dims=[self._dim_ve],
scope="item_MLP",
reuse=True,
),
)
self._add_module(
"n_item_bias",
LatentFactor(
l2_reg=self._l2_reg,
init="zero",
ids=self._get_input("n_item_id"),
shape=[self._max_item, 1],
scope="item_bias",
reuse=True,
),
)
else:
self._add_module(
"item_lf",
LatentFactor(
init="normal",
l2_reg=self._l2_reg,
ids=self._get_input("item_id", train=train),
shape=[self._max_item, self._dim_embed - self._dim_ve],
scope="item",
reuse=True,
),
train=False,
)
self._add_module(
"item_vf",
MultiLayerFC(
in_tensor=self._get_input("item_vfeature", train=train),
dims=[self._dim_ve],
scope="item_MLP",
reuse=True,
),
train=False,
)
self._add_module(
"item_bias",
LatentFactor(
l2_reg=self._l2_reg,
init="zero",
ids=self._get_input("item_id", train=train),
shape=[self._max_item, 1],
scope="item_bias",
reuse=True,
),
train=False,
)