Session Recommendation Model Based on Context-Aware and Gated Graph Neural Networks
Table 2
Performance comparison of different models on two datasets (%).
Method
Yoochoose1/64
Yoochoose1/4
Diginetica
P@20
MRR@20
P@20
MRR@20
P@20
MRR@20
POP
6.71
1.65
1.33
0.30
0.89
0.23
S-POP
30.44
18.35
27.08
17.75
21.06
13.68
BPR-MF
31.31
12.08
3.40
1.57
5.24
1.98
Item-KNN
51.60
21.81
52.31
21.70
35.75
11.57
FPMC
45.62
15.01
—
—
26.53
6.95
GRU4REC
60.64
22.89
59.53
22.60
29.45
8.33
NARM
68.32
28.63
69.73
29.23
49.70
16.17
STAMP
68.74
29.67
70.44
30.00
45.64
14.32
SR-GNN
69.53
30.41
70.90
30.43
49.70
16.31
CA-GGNN
70.84
31.83
72.93
32.91
51.12
18.48
Improve
1.31
1.42
2.03
2.48
1.42
2.17
Bold shows the experimental result of the model proposed in this paper. The results of FPMC experiments on Yoochoose1/4 datasets were not published because the FPMC model could not be initialized due to insufficient memory.
