Table 1: The search space of hyper-parameters and the optimal settings found by grid search for all baselines on the four real-world datasets.
Parameter	ML	Be	Ye	QK	Search Space
FPMC	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-3	10-3	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-3	10-3	10-2	{10-4,10-3,10-2,10-1}
input_length	10	5	5	10	{5,10,20,30,40,50}
batch_size	1024	512	512	1024	{128,256,512,1024}
Caser	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-3	10-3	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-3	5	10	{5,10,20,30,40,50}
input_length	5	5	5	5	{128,256,512,1024}
batch_size	512	256	256	512	{4,8,16,32,64}
horizontal_filter_num	16	16	16	32	{1,2,4,8,16}
vertical_filter_num	4	4	4	8	{16,32,64,128}
GRU4Rec	embedding_size	64	64	64	128	{10-4,10-3,10-2,10-1}
learning_rate	10-2	10-2	10-2	10-2	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-2	10-2	10-2	{5,10,20,30,40,50}
input_length	10	5	5	10	{128,256,512,1024}
batch_size	1024	512	512	1024	{128,256,512,1024}
GRU_unit_number	256	256	256	512	{128,256,512,1024}
SASRec	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-2	10-2	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-2	10-2	10-2	{10-4,10-3,10-2,10-1}
input_length	50	20	20	40	{5,10,20,30,40,50}
batch_size	128	128	512	1024	{128,256,512,1024}
selfattention_head_num	2	1	2	4	{1,2,4,8}
selfattention_block_num	1	1	1	2	{1,2,3,4}
BERT4Rec	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-4	10-4	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-2	10-2	10-2	{10-4,10-3,10-2,10-1}
input_length	50	20	20	50	{5,10,20,30,40,50}
batch_size	256	256	512	1024	{128,256,512,1024}
selfattention_head_num	4	1	2	4	{1,2,3,4}
selfattention_block_num	4	1	1	2	{1,2,3,4}
MAGNN	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-3	10-3	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-2	10-2	10-2	{10-4,10-3,10-2,10-1}
input_length	5	5	5	5	{5,10,20,30,40,50}
batch_size	1024	512	512	1024	{128,256,512,1024}
GNN_layer_num	2	2	2	2	{1,2,3,4}
memory_unit_num	10	10	10	20	{5,10,15,20}
BERD	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-3	10-3	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-2	5	10	{5,10,20,30,40,50}
input_length	5	5	5	5	{128,256,512,1024}
batch_size	1024	512	512	1024	{1,2,3,4}
selfattention_head_num	2	2	2	2	{1,2,3,4}
selfattention_block_num	1	1	1	2	{1,2,3,4}
filter_ratio	0.05	0.05	0.05	0.10	{0.05,0.10,0.15,0.20,0.25}
sample_size_of_Lsam	4	4	4	4	{1,2,3,4}
FMLP-Rec	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-3	10-3	10-3	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-3	10-3	10-2	{10-4,10-3,10-2,10-1}
input_length	50	20	20	40	{5,10,20,30,40,50}
batch_size	1024	256	256	1024	{128,256,512,1024}
learnable_filter_block_num	2	2	2	2	{1,2,3,4}
STEAM	embedding_size	64	64	64	128	{16,32,64,128}
learning_rate	10-3	10-3	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-3	7	10-2	{10-4,10-3,10-2,10-1}
input_length	50	20	20	40	{5,10,20,30,40,50}
batch_size	1024	256	256	1024	{128,256,526,1024}
selfattention_head_num	2	1	1	2	{1,2,3,4}
selfattention_block_num	1	1	1	2	{1,2,3,4}
insertion probability	0.2	0.4	0.4	0.2	{0.1,0.2,0.3,0.4,0.5}
deletion probability	0.1	0.1	0.1	0.1	{0.1,0.2,0.3,0.4,0.5}
mask probability	0.4	0.5	0.5	0.3	{0.1,0.2,0.3,0.4,0.5}
BirrRec	embedding_size	64	64	64	64	{16,32,64,128}
learning_rate	10-3	10-3	10-2	10-3	{10-4,10-3,10-2,10-1}
L2 Regularization coefficient	10-2	10-2	10-2	10-2	{10-4, 10-3, 10-2, 10-1}
input_length	5	5	5	5	{5,10,20,30,40,50}
batch_size	1024	1024	1024	1024	{128,256,512,1024}
threshold β in DRUT	10-1	10-1	10-1	10-1	{10-4,10-3,10-2,10-1,100}
threshold β' in DDUI	10-1	10-1	10-1	10-1	{10-4,10-3,10-2,10-1,100}
size K of rectification pools	10	10	10	10	{5,10,15,20,25}
exponential decay rate ρ	0.9	0.9	0.9	0.9	{0.1,0.3,0.5,0.7,0.9}