Hyperparameter ranges for all considered baseline models

Algorithms	topK	shrink	Similarity	Normalize	α	β	ℓ2 norm
kNN algorithms	[5 to 1000]	[0 to 1000]	[cosine, jaccard, asymmetric, dice, tversky]	[True, False]	—	—	—
Graph based algorithms	[5 to 1000]	—	—	—	[0 to 2], strategy: uniform distribution	[0 to 2], strategy: uniform distribution	—
EASER	—	—	—	—	—	—	[1 to 1e7], strategy: log-uniform distribution

Optimal hyperparameter values

Algorithms	topK	shrink	Similarity	Normalize	α	β	ℓ2 norm
ItemkNN	508	1000	cosine	True	—	—	—
UserkNN	146	1000	cosine	True	—	—	—
P3α	777	—	—	False	1.087096950563704	—	—
RP3β	777	—	—	True	0.5663562161452378	0.001085447926739258	—
EASER	—	—	—	—	—	—	1e3

Statistics of the provided train-test splits

Users	Items	Interactions	Density	Data leakage	Interactions per usermin	Interactions per itemmin	Interactions per usermax	Interactions per itemmax	Testing users
114,737	30,040	1,781,093	0.00052	0	10	5	393	3902	114,737

Full result table

Algorithms	R@1	R@5	R@10	R@20	R@100	N@1	N@5	N@10	N@20	N@100	T-Time(s)	P-Time(s)	Average P-Time Per User(ms)
Random	0	0.000	0.000	0.001	0.003	0	0.0002	0.0002	0.0004	0.0010	0	191.891	1.501
TopPop	0.002	0.009	0.017	0.031	0.104	0.007	0.008	0.012	0.017	0.036	0.022	172.280	1.501
ItemkNN	0.017	0.055	0.084	0.123	0.253	0.058	0.054	0.066	0.080	0.115	140.771	201.694	1.757
UserkNN	0.012	0.038	0.057	0.084	0.174	0.041	0.038	0.046	0.055	0.079	381.969	162.805	1.418
P3α	0.011	0.033	0.049	0.069	0.151	0.037	0.033	0.040	0.047	0.068	37.596	170.240	1.483
RP3β	0.016	0.051	0.076	0.110	0.223	0.054	0.050	0.061	0.073	0.103	38.748	173.301	1.510
EASE	0.016	0.051	0.076	0.110	0.223	0.054	0.050	0.061	0.073	0.103	316.519	206.727	1.801
KGIN	0.007	0.033	0.058	0.097	0.097	0.022	0.029	0.040	0.054	0.054	117360.426	362.042	3.15