algorithm specific settings.rst

Algorithm Specific Settings

--init_method

parameter initializers

• default: initialize parameters according to the original paper
• normal: initialize parameters with normal distribution
• uniform: initialize parameters with uniform distribution
• xavier_normal: initialize parameters with xavier_normal distribution
• xavier_uniform: initialize parameters with xavier_uniform distribution

--optimizer

optimization method for training the algorithms

• default (optimizer in the original paper)
• sgd
• adam
• adagrad

--early_stop

whether to activate the early-stop mechanism

• true
• false

--tune_testset

whether to directly tune on testset, and the default value is false

• true
• false

--factors

the dimension of latent factors (embeddings)

--reg_1

the coefficient of L1 regularization

--reg_2

the coefficient of L2 regularization

--dropout

dropout rate

--lr

learning rate

--epochs

training epochs

--batch_size

batch size for training

--num_layers

number of layers for MLP

--alpha

constant to multiply the penalty terms for SLIM

--elastic

the ElasticNet mixing parameter for SLIM in the range of (0,1)

--pop_n

the preliminary selected top-n popular candidate items to reduce the time complexity for MostPop

--maxk

the number of neighbors to take into account for ItemKNN

--node_dropout

node dropout ratio for NGCF

--mess_dropout

message dropout ratio for NGCF

--kl_reg

the coefficient of KL regularization for Multi-VAE