-
Notifications
You must be signed in to change notification settings - Fork 12
/
Copy pathparameter_setting.txt
73 lines (56 loc) · 1.58 KB
/
parameter_setting.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
Algorithm - default parameters
For details about the parameters please refer to the corresponding paper.
TBP:
k=5 (number of predicted items)
LST:
k=5 (number of predicted items)
TOP:
k=5 (number of predicted items)
MC:
k=5 (number of predicted items)
CLF:
k=5 (number of predicted items)
C. Cumby, A. Fano, R. Ghani, and M. Krema,
''Predicting customer shopping lists from point-of-sale purchase data''
NMF:
n_factor=100
alpha=0
l1_ratio=0
beta=1
max_iter=100
tol=1e-4
D. D. Lee and H. S. Seung,
''Algorithms for non-negative matrix factorization''
FMC:
n_factor=100
alpha=0.01
lambdas=(0.001, 0.001, 0.001, 0.001)
std=0.01
n_epoch=1000
tolerance=1.0e-8
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme,
''Factorizing personalized markov chains for next-basket recommendation''
HRM:
neg_samples=25
n_epoch=100
alpha=0.01
lambda_r=0.001
decay=0.9
drop=0.6
P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, and X. Cheng,
''Learning hierarchical representation model for nextbasket recommendation''
DRM:
alpha = 0.01
decay = 100.0
lambda_r = 0.001
n_hidden = 3
embedding_dimension = 10
n_epoch = 100
F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan,
''A dynamic recurrent model for next basket recommendation''
Train-Test split:
min_number_of_basket=10 (minimum number of baskets)
min_basket_size=1 (minimum basket size)
max_basket_size=float('inf') (maximum basket size)
min_item_occurrences=2 (minimum number of occurrences of an item)
There are not specific computer memory requirements.