class ItrMLP(Recommender):
def __init__(self,
batch_size,
dim_embed,
max_user,
max_item,
pretrained_user_embeddings,
pretrained_item_embeddings,
user_dims,
item_dims,
test_batch_size=None,
l2_reg=None,
opt='SGD',
sess_config=None):
self._dim_embed = dim_embed
self._pretrained_user_embeddings = pretrained_user_embeddings
self._pretrained_item_embeddings = pretrained_item_embeddings
self._user_dims = user_dims
self._item_dims = item_dims
super(ItrMLP, self).__init__(batch_size=batch_size,
test_batch_size=test_batch_size,
max_user=max_user,
max_item=max_item,
l2_reg=l2_reg,
opt=opt,
sess_config=sess_config)
def _initialize(self, init_dict):
super(ItrMLP, self)._initialize(init_dict=init_dict)
print(colored('[Pretrain user MLP into identity]', 'blue'))
self._user_vec.pretrain_mlp_as_identity(self._sess)
print(colored('[Pretrain item MLP into identity]', 'blue'))
self._item_vec.pretrain_mlp_as_identity(self._sess)
def update_embeddings(self):
self._user_vec.forward_update_embeddings(self._sess)
self._item_vec.forward_update_embeddings(self._sess)
def _input_mappings(self, batch_data, train):
if train:
return {
self._user_id_input: batch_data['user_id_input'],
self._item_id_input: batch_data['item_id_input'],
self._labels: batch_data['labels']
}
else:
return {
self._user_id_serving: batch_data['user_id_input'],
self._item_id_serving: batch_data['item_id_input']
}
def _build_user_inputs(self, train=True):
if train:
self._user_id_input = self._input(dtype='int32',
shape=[self._batch_size],
name='user_id_input')
else:
self._user_id_serving = self._input(dtype='int32',
shape=[self._test_batch_size],
name='user_id_serving')
def _build_item_inputs(self, train=True):
if train:
self._item_id_input = self._input(dtype='int32',
shape=[self._batch_size],
name='item_id_input')
else:
self._item_id_serving = self._input(dtype='int32',
shape=[self._test_batch_size],
name='item_id_serving')
def _build_extra_inputs(self, train=True):
if train:
self._labels = self._input(dtype='float32',
shape=[self._batch_size],
name='labels')
def _build_user_extractions(self, train=True):
if train:
self._user_vec = TemporalLatentFactor(
l2_reg=self._l2_reg,
init=self._pretrained_user_embeddings,
ids=self._user_id_input,
mlp_dims=self._user_dims,
shape=[self._max_user, self._dim_embed],
train=True,
scope='user',
reuse=False)
self._loss_nodes += [self._user_vec]
else:
self._user_vec_serving = TemporalLatentFactor(
l2_reg=self._l2_reg,
init=self._pretrained_user_embeddings,
ids=self._user_id_serving,
mlp_dims=self._user_dims,
shape=[self._max_user, self._dim_embed],
train=False,
scope='user',
reuse=True)
def _build_item_extractions(self, train=True):
if train:
self._item_vec = TemporalLatentFactor(
l2_reg=self._l2_reg,
init=self._pretrained_item_embeddings,
ids=self._item_id_input,
mlp_dims=self._item_dims,
shape=[self._max_item, self._dim_embed],
train=True,
scope='item',
reuse=False)
self._item_bias = LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._item_id_input,
shape=[self._max_item, 1],
scope='item_bias',
reuse=False)
self._loss_nodes += [self._item_vec, self._item_bias]
else:
self._item_vec_serving = TemporalLatentFactor(
l2_reg=self._l2_reg,
init=self._pretrained_item_embeddings,
ids=self._item_id_serving,
mlp_dims=self._item_dims,
shape=[self._max_item, self._dim_embed],
train=False,
scope='item',
reuse=True)
self._item_bias_serving = LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._item_id_serving,
shape=[self._max_item, 1],
scope='item_bias',
reuse=True)
def _build_default_interactions(self, train=True):
if train:
self._interaction_train = PointwiseMSE(
user=self._user_vec.get_outputs()[0],
item=self._item_vec.get_outputs()[0],
item_bias=self._item_bias.get_outputs()[0],
labels=self._labels,
a=1.0,
b=1.0,
sigmoid=True,
train=True,
scope='PointwiseMSE',
reuse=False)
self._loss_nodes.append(self._interaction_train)
else:
self._interaction_serve = PointwiseMSE(
user=self._user_vec_serving.get_outputs()[0],
item=self._item_vec_serving.get_outputs()[0],
item_bias=self._item_bias_serving.get_outputs()[0],
sigmoid=True,
train=False,
batch_serving=False,
scope='PointwiseMSE',
reuse=True)
def _build_serving_graph(self):
super(ItrMLP, self)._build_serving_graph()
self._scores = self._interaction_serve.get_outputs()[0]
class PMF(Recommender):
def __init__(self,
batch_size,
dim_embed,
max_user,
max_item,
test_batch_size=None,
l2_reg=None,
opt='SGD',
sess_config=None):
self._dim_embed = dim_embed
super(PMF, self).__init__(batch_size=batch_size,
test_batch_size=test_batch_size,
max_user=max_user,
max_item=max_item,
l2_reg=l2_reg,
opt=opt,
sess_config=sess_config)
def _input_mappings(self, batch_data, train):
if train:
return {
self._user_id_input: batch_data['user_id_input'],
self._item_id_input: batch_data['item_id_input'],
self._labels: batch_data['labels']
}
else:
return {self._user_id_serving: batch_data['user_id_input']}
def _build_user_inputs(self, train=True):
if train:
self._user_id_input = self._input(dtype='int32',
shape=[self._batch_size],
name='user_id_input')
else:
self._user_id_serving = self._input(dtype='int32',
shape=[None],
name='user_id_serving')
def _build_item_inputs(self, train=True):
if train:
self._item_id_input = self._input(dtype='int32',
shape=[self._batch_size],
name='item_id_input')
else:
self._item_id_serving = None
def _build_extra_inputs(self, train=True):
if train:
self._labels = self._input(dtype='float32',
shape=[self._batch_size],
name='labels')
def _build_user_extractions(self, train=True):
if train:
self._user_vec = LatentFactor(
l2_reg=self._l2_reg,
init='normal',
ids=self._user_id_input,
shape=[self._max_user, self._dim_embed],
scope='user',
reuse=False)
self._loss_nodes += [self._user_vec]
else:
self._user_vec_serving = LatentFactor(
l2_reg=self._l2_reg,
init='normal',
ids=self._user_id_serving,
shape=[self._max_user, self._dim_embed],
scope='user',
reuse=True)
def _build_item_extractions(self, train=True):
if train:
self._item_vec = LatentFactor(
l2_reg=self._l2_reg,
init='normal',
ids=self._item_id_input,
shape=[self._max_item, self._dim_embed],
scope='item',
reuse=False)
self._item_bias = LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._item_id_input,
shape=[self._max_item, 1],
scope='item_bias',
reuse=False)
self._loss_nodes += [self._item_vec, self._item_bias]
else:
self._item_vec_serving = LatentFactor(
l2_reg=self._l2_reg,
init='normal',
ids=self._item_id_serving,
shape=[self._max_item, self._dim_embed],
scope='item',
reuse=True)
self._item_bias_serving = LatentFactor(l2_reg=self._l2_reg,
init='zero',
ids=self._item_id_serving,
shape=[self._max_item, 1],
scope='item_bias',
reuse=True)
def _build_default_interactions(self, train=True):
if train:
self._interaction_train = PointwiseMSE(
user=self._user_vec.get_outputs()[0],
item=self._item_vec.get_outputs()[0],
item_bias=self._item_bias.get_outputs()[0],
labels=self._labels,
a=1.0,
b=1.0,
train=True,
scope='PointwiseMSE',
reuse=False)
self._loss_nodes.append(self._interaction_train)
else:
self._interaction_serve = PointwiseMSE(
user=self._user_vec_serving.get_outputs()[0],
item=self._item_vec_serving.get_outputs()[0],
item_bias=self._item_bias_serving.get_outputs()[0],
train=False,
scope='PointwiseMSE',
reuse=True)
def _build_serving_graph(self):
super(PMF, self)._build_serving_graph()
self._scores = self._interaction_serve.get_outputs()[0]