Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Table 1

Default main hyper parameters setting.

Modules	Main hyper parameters	Value
LDA	Number of topics in LDA	20
	Dirichlet parameter	0.1
Node2vec	Length of Node2vec walk	80
	Number of Node2vec walks	10
	Window size of Node2vec walk	10
GCN	Number of layers	2
	Size of layer convolution layer	64
Wide & deep	Number of layers	2
	Dimensions	128
GraphSAGE	Neighborhood deep neighborhood sample sizes	2
		25
NGCF	Embedding propagation layers	3
	Depth of the NGCF	3
	Top-k	20
PinSage	Top-k	2
	Size of hidden dimension size	512
KGAT	Top-k	20
	Tower structure of hidden layer size	512/256/128/64
GraphRec	Size of the hidden layer	128
	Number of three hidden layers	3
NIA-GCN	Size of first layer/second layer	40/2
	Dimension of each layer	64