Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2357-2364.DOI: 10.11772/j.issn.1001-9081.2023081063
• Artificial intelligence • Previous Articles Next Articles
Tingjie TANG1,2, Jiajin HUANG3(), Jin QIN1,2, Hui LU1,2
Received:
2023-08-07
Revised:
2023-10-10
Accepted:
2023-10-17
Online:
2023-12-18
Published:
2024-08-10
Contact:
Jiajin HUANG
About author:
TANG Tingjie , born in 1999, M. S. candidate. His researchinterests include recommender system.Supported by:
通讯作者:
黄佳进
作者简介:
唐廷杰(1999—),男,贵州黔东南人,硕士研究生,主要研究方向:推荐系统基金资助:
CLC Number:
Tingjie TANG, Jiajin HUANG, Jin QIN, Hui LU. Session-based recommendation based on graph co-occurrence enhanced multi-layer perceptron[J]. Journal of Computer Applications, 2024, 44(8): 2357-2364.
唐廷杰, 黄佳进, 秦进, 陆辉. 基于图共现增强多层感知机的会话推荐[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2357-2364.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023081063
数据集 | 点击数 | 训练 会话数 | 测试 会话数 | 项目数 | 会话 平均 长度 | 最大 会话 长度 |
---|---|---|---|---|---|---|
Diginetica | 982 961 | 719 470 | 60 858 | 43 097 | 5.12 | 70 |
Yoochoose 1/64 | 557 248 | 369 859 | 55 898 | 16 766 | 6.16 | 146 |
Nowplaying | 1 367 963 | 825 304 | 89 824 | 60 417 | 7.42 | 30 |
Tab. 1 Statistical information of datasets
数据集 | 点击数 | 训练 会话数 | 测试 会话数 | 项目数 | 会话 平均 长度 | 最大 会话 长度 |
---|---|---|---|---|---|---|
Diginetica | 982 961 | 719 470 | 60 858 | 43 097 | 5.12 | 70 |
Yoochoose 1/64 | 557 248 | 369 859 | 55 898 | 16 766 | 6.16 | 146 |
Nowplaying | 1 367 963 | 825 304 | 89 824 | 60 417 | 7.42 | 30 |
方法 | Diginetica | Yoochoose 1/64 | Nowplaying | |||
---|---|---|---|---|---|---|
P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | |
POP | 0.89 | 0.20 | 6.71 | 1.65 | 2.28 | 0.86 |
Item-KNN | 35.75 | 11.57 | 51.60 | 21.81 | 15.94 | 4.91 |
FPMC | 22.53 | 6.95 | 45.62 | 15.01 | 7.36 | 2.82 |
GRU4REC | 29.45 | 8.33 | 60.64 | 22.89 | 7.92 | 4.48 |
NARM | 49.70 | 16.17 | 68.32 | 28.63 | 18.59 | 6.93 |
STAMP | 45.64 | 14.32 | 68.74 | 29.67 | 17.66 | 6.88 |
SR-GNN | 51.26 | 17.66 | 70.57 | 30.94 | 17.76 | 7.49 |
TAGNN | 51.53 | 17.90 | 71.02 | 31.12 | 19.02 | 7.82 |
S2-DHCN | 53.66 | 18.51 | 70.74 | 30.16 | 23.50 | 8.18 |
Disen-GNN | 18.99 | 22.22 | ||||
FMLP-Rec | 51.94 | 17.44 | 71.29 | 30.01 | 8.12 | |
GCE-MLP | 54.08 | 71.77 | 31.78 | 22.26 | 8.55 |
Tab. 2 Performance comparison of different methods on three datasets
方法 | Diginetica | Yoochoose 1/64 | Nowplaying | |||
---|---|---|---|---|---|---|
P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | |
POP | 0.89 | 0.20 | 6.71 | 1.65 | 2.28 | 0.86 |
Item-KNN | 35.75 | 11.57 | 51.60 | 21.81 | 15.94 | 4.91 |
FPMC | 22.53 | 6.95 | 45.62 | 15.01 | 7.36 | 2.82 |
GRU4REC | 29.45 | 8.33 | 60.64 | 22.89 | 7.92 | 4.48 |
NARM | 49.70 | 16.17 | 68.32 | 28.63 | 18.59 | 6.93 |
STAMP | 45.64 | 14.32 | 68.74 | 29.67 | 17.66 | 6.88 |
SR-GNN | 51.26 | 17.66 | 70.57 | 30.94 | 17.76 | 7.49 |
TAGNN | 51.53 | 17.90 | 71.02 | 31.12 | 19.02 | 7.82 |
S2-DHCN | 53.66 | 18.51 | 70.74 | 30.16 | 23.50 | 8.18 |
Disen-GNN | 18.99 | 22.22 | ||||
FMLP-Rec | 51.94 | 17.44 | 71.29 | 30.01 | 8.12 | |
GCE-MLP | 54.08 | 71.77 | 31.78 | 22.26 | 8.55 |
变体模型 | Diginetica | Yoochoose 1/64 | Nowplaying | |||
---|---|---|---|---|---|---|
P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | |
GCE-MLP-C | 53.59 | 18.71 | 71.42 | 31.50 | 22.03 | 8.12 |
GCE-MLP-NCL | 53.14 | 18.60 | 71.20 | 31.54 | 22.15 | 7.94 |
GCE-MLP-SMG | 53.58 | 18.61 | 71.68 | 30.50 | 22.20 | 7.66 |
GCE-MLP-N | 53.79 | 18.67 | 71.44 | 31.54 | 21.98 | 8.48 |
GCE-MLP | 54.08 | 18.87 | 71.77 | 31.78 | 22.26 | 8.55 |
Tab. 3 Performance comparison of GCE-MLP variants
变体模型 | Diginetica | Yoochoose 1/64 | Nowplaying | |||
---|---|---|---|---|---|---|
P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | |
GCE-MLP-C | 53.59 | 18.71 | 71.42 | 31.50 | 22.03 | 8.12 |
GCE-MLP-NCL | 53.14 | 18.60 | 71.20 | 31.54 | 22.15 | 7.94 |
GCE-MLP-SMG | 53.58 | 18.61 | 71.68 | 30.50 | 22.20 | 7.66 |
GCE-MLP-N | 53.79 | 18.67 | 71.44 | 31.54 | 21.98 | 8.48 |
GCE-MLP | 54.08 | 18.87 | 71.77 | 31.78 | 22.26 | 8.55 |
方法 | Diginetica | Yoochoose 1/64 | Nowplaying | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Short | Long | Short | Long | Short | Long | |||||||
P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | |
Disen-GNN | 54.45 | 19.69 | 51.50 | 16.74 | 72.90 | 33.60 | 67.32 | 26.16 | 20.96 | 7.62 | 23.57 | 8.84 |
FMLP-Rec | 53.21 | 18.64 | 49.33 | 15.01 | 72.99 | 32.46 | 67.86 | 25.33 | 21.82 | 7.84 | 24.52 | 8.74 |
GCE-MLP-C | 54.51 | 19.46 | 50.78 | 16.33 | 72.99 | 33.72 | 67.73 | 26.31 | 21.43 | 7.79 | 24.26 | 8.72 |
GCE-MLP-NCL | 54.03 | 19.40 | 50.21 | 16.30 | 72.65 | 33.72 | 67.80 | 26.64 | 21.45 | 7.46 | 24.15 | 8.56 |
GCE-MLP | 54.84 | 19.57 | 51.69 | 16.61 | 73.24 | 33.86 | 68.31 | 26.91 | 21.04 | 8.05 | 24.46 | 9.46 |
Tab. 4 Performance of different methods with different session lengths evaluated in terms of P@20 and MRR@20
方法 | Diginetica | Yoochoose 1/64 | Nowplaying | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Short | Long | Short | Long | Short | Long | |||||||
P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | P@20 | MRR@20 | |
Disen-GNN | 54.45 | 19.69 | 51.50 | 16.74 | 72.90 | 33.60 | 67.32 | 26.16 | 20.96 | 7.62 | 23.57 | 8.84 |
FMLP-Rec | 53.21 | 18.64 | 49.33 | 15.01 | 72.99 | 32.46 | 67.86 | 25.33 | 21.82 | 7.84 | 24.52 | 8.74 |
GCE-MLP-C | 54.51 | 19.46 | 50.78 | 16.33 | 72.99 | 33.72 | 67.73 | 26.31 | 21.43 | 7.79 | 24.26 | 8.72 |
GCE-MLP-NCL | 54.03 | 19.40 | 50.21 | 16.30 | 72.65 | 33.72 | 67.80 | 26.64 | 21.45 | 7.46 | 24.15 | 8.56 |
GCE-MLP | 54.84 | 19.57 | 51.69 | 16.61 | 73.24 | 33.86 | 68.31 | 26.91 | 21.04 | 8.05 | 24.46 | 9.46 |
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