Hyperparameters for gru4rec (fixed layer size of 100)

We tested the following hyperparameter space:

Parameter From To Steps
Loss Function BPR-MAX TOP1-MAX -
Final Activation Function ELU-0.5 Linear -
Learning Rate 0.1
0.5		 0.01
0.1		 10
5
Momentum 0.00 0.90 0.10
Drop-Out 0.00 0.90 0.10
Constrained Embedding True False -

Dataset Loss Function Final Activation Function Learning Rate Momentum Drop-Out Constrained Embedding
RSC15/4 BPR-MAX Linear 0.09 0.0 0.1 True
RSC15/64 BPR-MAX Linear 0.05 0.3 0.1 False
DIGINETICA BPR-MAX ELU-0.5 0.02 0.5 0.1 True
DIGINETICA (STAMP) BPR-MAX ELU-0.5 0.05 0.1 0.2 True

Hyperparameters for stamp (fixed layer size of 100)

We tested the following hyperparameter space:

Parameter From To Steps
Number of Epochs 10 30 10
Decay Rate 0.0 0.9 10
Initial Learning Rate 0.001
0.0001		 0.01
0.001		 10
10

Dataset Number of Epochs Decay Rate Initial Learning Rate
RSC15/4 20 0.2 0.0002
RSC15/64 30 0.4 0.0004
DIGINETICA 10 0.3 0.0007
DIGINETICA (STAMP) 30 0.9 0.0004

Hyperparameters for narm (fixed factors' number of 100, layer size of of 100, and epochs' number of 20)

We tested the following hyperparameter space:

Parameter From To Steps
Learning Rate 0.1
0.5		 0.01
0.1		 10
5

Dataset Learning Rate
RSC15/4 0.002
RSC15/64 0.005
DIGINETICA 0.0007
DIGINETICA (STAMP) 0.002

Hyperparameters for nextitnet (?)

We tested the following hyperparameter space:

Parameter From To Steps
Learning Rate 0.01
0.001		 0.001
0.0001		 10
5
Iterations 10 30 10
Negative Sampling True False -

Dataset Learning Rate Iterations Negative Sampling
RSC15/64 0.001 10 False

Hyperparameters for ar
fixed pruning size of 20

Hyperparameters for sr (fixed pruning size of 100)

We tested the following hyperparameter space:

Parameter From To Steps Options
Steps 1 20 1 -
Weighting - - - Div, Linear, Quadratic, Log, Same

Dataset Steps Weighting
RSC15/4 2 Log
RSC15/64 4 Quadratic
DIGINETICA 15 Div
DIGINETICA (STAMP) 8 Quadratic

Hyperparameters for sknn (fixed sampling mode of recent)

We tested the following hyperparameter space:

Parameter Options
Number of Neighbors 50, 100, 500, 1000, 1500
Sample Size 500, 1000, 2500, 5000, 10000
Similarity Cosine, Jaccard

Dataset Number of Neighbors Sample Size Similarity
RSC15/4 500 500 Jaccard
RSC15/64 500 1000 Cosine
DIGINETICA 50 500 Cosine
DIGINETICA (STAMP) 100 500 Cosine

Hyperparameters for vsknn (fixed sampling mode of recent)

We tested the following hyperparameter space:

Parameter Options
Number of Neighbors 50, 100, 500, 1000, 1500
Sample Size 500, 1000, 2500, 5000, 10000
Weighting Same, Div, Linear, Quadratic, Log
Weighting Score Same, Div, Linear, Quadratic, Log
IDF Weighting False, 1, 2, 5, 10

Dataset Number of Neighbors Sample Size Weighting Weighting Score IDF_Weighting
RSC15/4 1000 1000 Log Quadratic 5
RSC15/64 1000 5000 Log Quadratic 2
DIGINETICA 500 5000 Quadratic Div 10
DIGINETICA (STAMP) 500 10000 Quadratic Quadratic 10

Hyperparameters for vsknn

We tested the following hyperparameter space:

Parameter Options
Expert StdExpert, DirichletExpert
Max Considered Context Length 5,10,20,30,40,50,75
Number of Recent Candidates (Only for Adaptive Configuration) 5,10,20,30,40,50,75

Dataset Expert Max Considered Context Length Number of Recent Candidates
RSC15/4, RSC15/64, DIGINETICA, DIGINETICA(STAMP) StdExpert 50 1000

Hyperparameters for sgnn (fixed layer size of 100)

We tested the following hyperparameter space:

Parameter From To Steps
Learning Rate 0.01
0.001		 0.001
0.0001		 10
10
L2 Penalty 0.0001
0.00001		 0.00001
0.000001		 10
10
Decay Rate 0.1 0.9 10
Decay Rate Step 3 7 2

Dataset Learning Rate L2 Penalty Decay Rate Decay Rate Step
RSC15/4
RSC15/64 0.008 0.0001 0.45 3
DIGINETICA 0.0002 0.00007 0.1 5
DIGINETICA (STAMP)