def parse_model_args(parser, model_name='PreHash'):
parser.add_argument('--hash_u_num',
type=int,
default=128,
help='Size of user hash.')
parser.add_argument('--sample_max_n',
type=int,
default=128,
help='Sample top-n when learn hash.')
parser.add_argument('--sample_r_n',
type=int,
default=128,
help='Sample random-n when learn hash.')
parser.add_argument('--hash_layers',
type=str,
default='[32]',
help='MLP layer sizes of hash')
parser.add_argument(
'--tree_layers',
type=str,
default='[64]',
help='Number of branches in each level of the hash tree')
parser.add_argument(
'--transfer_att_size',
type=int,
default=16,
help=
'Size of attention layer of transfer layer (combine the hash and cf vector)'
)
parser.add_argument('--cs_ratio',
type=float,
default=0.1,
help='Cold-Sampling ratio of each batch.')
return RecModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='GRU4Rec'):
parser.add_argument('--hidden_size',
type=int,
default=64,
help='Size of hidden vectors in GRU.')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of GRU layers.')
parser.add_argument('--p_layers',
type=str,
default='[64]',
help="Size of each layer.")
parser.add_argument(
'--neg_emb',
type=int,
default=1,
help="Whether use negative interaction embeddings.")
parser.add_argument(
'--neg_layer',
type=str,
default='[]',
help=
"Whether use a neg_layer to transfer negative interaction embeddings. "
"[] means using -v. It is ignored when neg_emb=1")
return RecModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='NeuMF'):
parser.add_argument('--layers',
type=str,
default='[64]',
help="Size of mlp layers.")
parser.add_argument('--p_layers',
type=str,
default='[]',
help="Size of prediction mlp layers.")
return RecModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='ACCM'):
parser.add_argument('--f_vector_size',
type=int,
default=64,
help='Size of feature vectors.')
parser.add_argument('--cb_hidden_layers',
type=str,
default='[]',
help="Number of CB part's hidden layer.")
parser.add_argument('--attention_size',
type=int,
default=16,
help='Size of attention layer.')
parser.add_argument('--cs_ratio',
type=float,
default=0.1,
help='Cold-Sampling ratio of each batch.')
return RecModel.parse_model_args(parser, model_name)
