def __init__(self, args):
self.args = args
self.batch_size = args.batch_size
self.epoch = args.epoch
self.verbose = args.verbose
self.keep = args.keep
self.TopK = args.TopK
# Data loading
self.data = DATA.LoadData(self.args.path, self.args.dataset) #获取数据
self.n_user = self.data.n_user
self.n_item = self.data.n_item
self.valid_dimension = self.data.Train_data.shape[1] - 1
if args.verbose > 0:
print(
"AFM: dataset=%s, factors=%s, #epoch=%d, batch=%d, lr=%.4f, lamda_attention=%.1e, keep=%s, optimizer=%s, batch_norm=%d"
% (args.dataset, args.hidden_factor, args.epoch,
args.batch_size, args.lr, args.lamda_attention, args.keep,
args.optimizer, args.batch_norm))
activation_function = tf.nn.relu
if args.activation == 'sigmoid':
activation_function = tf.sigmoid
elif args.activation == 'tanh':
activation_function == tf.tanh
elif args.activation == 'identity':
activation_function = tf.identity
# Training\\
self.model = AFM(self.n_user, self.n_item, self.data.features_M,
args.attention, eval(args.hidden_factor),
activation_function, args.lr, args.lamda_attention,
eval(args.keep), args.optimizer, args.decay,
self.valid_dimension)
def __init__(self, args):
self.args = args
self.batch_size = args.batch_size
self.epoch = args.epoch
self.verbose = args.verbose
self.keep = args.keep
self.TopK = args.TopK
# Data loading
self.data = DATA.LoadData(self.args.path, self.args.dataset) #获取数据
self.n_user = self.data.n_user
self.n_item = self.data.n_item
self.valid_dimension = self.data.Train_data.shape[1] - 1
if args.verbose > 0:
print(
"FM: dataset=%s, factors=%d, #epoch=%d, batch=%d, lr=%.4f, lambda=%.1e, keep=%.2f, optimizer=%s, batch_norm=%d"
% (args.dataset, args.hidden_factor, args.epoch,
args.batch_size, args.lr, args.lamda, args.keep,
args.optimizer, args.batch_norm))
# Training\\\
self.model = FM(self.valid_dimension, self.data.features_M,
self.n_user, self.n_item, args.hidden_factor, args.lr,
args.lamda, args.keep, args.optimizer, args.batch_norm,
args.verbose)
def __init__(self,args):
self.args = args
self.batch_size = args.batch_size
self.epoch = args.epoch
self.verbose = args.verbose
self.keep = args.keep
# Data loading
self.data = DATA.LoadData(self.args.path, self.args.dataset)#获取数据
self.n_user = self.data.n_user
self.n_item = self.data.n_item
if args.verbose > 0:
print("FM: dataset=%s, factors=%d, #epoch=%d, batch=%d, lr=%.4f, lambda=%.1e, keep=%.2f, optimizer=%s, batch_norm=%d"
%(args.dataset, args.hidden_factor, args.epoch, args.batch_size, args.lr, args.lamda, args.keep, args.optimizer, args.batch_norm))
# Training\\\
self.model = MF(self.n_user+self.n_item, self.n_user, self.n_item , args.hidden_factor,args.lr, args.lamda, args.keep, args.optimizer, args.batch_norm, args.verbose)
def __init__(self,args):
self.args = args
self.batch_size = args.batch_size
self.epoch = args.epoch
self.TopK = args.TopK
# Data loading
self.data = DATA.LoadData(self.args.path, self.args.dataset)#获取数据
self.n_user = self.data.n_user
self.n_item = self.data.n_item
self.features_M = self.data.features_M
self.valid_dimension = self.data.Train_data.shape[1] - 1
print("OurModel: dataset=%s, factors=%d, #epoch=%d, batch=%d, lr=%.4f, lambda=%.1e, keep=%.2f, optimizer=%s, batch_norm=%d"
%(args.dataset, args.hidden_factor, args.epoch, args.batch_size, args.lr, args.lamda, args.keep, args.optimizer, args.batch_norm))
# Training\\\建立模型
if args.dataset =='resturant': #contextual + time
self.context = True
self.time = True
self.time_dimension = 5
self.feature_dimension= self.valid_dimension - 2 - self.time_dimension
if args.dataset == 'tmall':
self.context = True
self.time = False
self.time_dimension = 0
self.feature_dimension= self.valid_dimension - 2 - self.time_dimension
if args.dataset =='frappe' or args.dataset =='fra':
self.context = True
self.time = False
self.feature_dimension= self.valid_dimension - 2
self.time_dimension = 0
if args.dataset =='jiaju':
self.context = True
self.time = True
self.time_dimension = 3
self.feature_dimension= self.valid_dimension - 2 - self.time_dimension
self.model = OUR(self.feature_dimension,self.time_dimension,self.features_M, self.n_user, self.n_item , args.hidden_factor,args.lr, args.lamda, args.optimizer, self.context, self.time)
def __init__(self, args):
self.args = args
self.batch_size = args.batch_size
self.epoch = args.epoch
self.TopK = args.TopK
# Data loading
self.data = DATA.LoadData(self.args.path, self.args.dataset) #获取数据
self.n_user = self.data.n_user
self.n_item = self.data.n_item
set1 = set()
for line in self.data.Total_data.values[:, 3:]:
set1.add(tuple(line))
self.feature_inject = {f: i for i, f in enumerate(set1)}
self.features_M = len(self.feature_inject.keys())
print(
"OurModel: dataset=%s, factors=%d, #epoch=%d, batch=%d, lr=%.4f, lambda=%.1e, keep=%.2f, optimizer=%s, batch_norm=%d"
%
(args.dataset, args.hidden_factor, args.epoch, args.batch_size,
args.lr, args.lamda, args.keep, args.optimizer, args.batch_norm))
self.model = CARS2(self.features_M, self.n_user, self.n_item,
args.hidden_factor, args.lr, args.lamda,
args.optimizer)