Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2711-2718.DOI: 10.11772/j.issn.1001-9081.2023091257
• Data science and technology • Previous Articles Next Articles
Tingjie TANG1,2, Jiajin HUANG3(), Jin QIN1,2
Received:
2023-09-13
Revised:
2023-12-14
Accepted:
2023-12-15
Online:
2024-03-19
Published:
2024-09-10
Contact:
Jiajin HUANG
About author:
TANG Tingjie, born in 1999, M. S. candidate. His research interests 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. Session-based recommendation with graph auxiliary learning[J]. Journal of Computer Applications, 2024, 44(9): 2711-2718.
唐廷杰, 黄佳进, 秦进. 基于图辅助学习的会话推荐[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2711-2718.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091257
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 |
---|---|---|---|---|---|
Diginetica | 982 961 | 719 470 | 60 858 | 43 097 | 5.12 |
Tmall | 818 479 | 351 268 | 25 898 | 40 728 | 6.69 |
Tab. 1 Statistical results 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 |
---|---|---|---|---|---|
Diginetica | 982 961 | 719 470 | 60 858 | 43 097 | 5.12 |
Tmall | 818 479 | 351 268 | 25 898 | 40 728 | 6.69 |
模型 | Diginetica | Tmall | ||||||
---|---|---|---|---|---|---|---|---|
P@10 | P@20 | M@10 | M@20 | P@10 | P@20 | M@10 | M@20 | |
GRU4Rec | 17.93 | 30.79 | 7.73 | 8.22 | 9.47 | 10.93 | 5.78 | 5.89 |
NARM | 35.44 | 48.32 | 15.13 | 16.00 | 19.17 | 23.30 | 10.42 | 10.70 |
STAMP | 33.69 | 46.62 | 14.26 | 15.13 | 22.63 | 26.47 | 13.12 | 13.36 |
SR-GNN | 38.42 | 51.26 | 16.89 | 17.78 | 23.41 | 27.57 | 13.45 | 13.72 |
GC-SAN | 38.91 | 51.90 | 17.31 | 18.21 | 19.21 | 23.30 | 10.67 | 10.96 |
FGNN | 37.72 | 50.58 | 15.95 | 16.84 | 20.67 | 25.24 | 10.07 | 10.39 |
GCE-GNN | 41.16 | 54.22 | 18.15 | 19.04 | 28.01 | 33.42 | 15.08 | 15.42 |
S2-DHCN | 40.21 | 53.66 | 17.59 | 18.51 | 26.22 | 31.42 | 14.60 | 15.05 |
Disen-GNN | 40.63 | 53.79 | 17.98 | 18.99 | 25.87 | 30.78 | 15.05 | 15.40 |
HyperS2Rec | 40.52 | 54.13 | 18.09 | 18.91 | 27.26 | 32.91 | 14.98 | 15.39 |
CGSNet | ||||||||
SR-GAL | 41.75 | 55.01 | 18.60 | 19.50 | 33.27 | 39.08 | 17.38 | 17.79 |
Tab. 2 Performance comparison of different models on two datasets
模型 | Diginetica | Tmall | ||||||
---|---|---|---|---|---|---|---|---|
P@10 | P@20 | M@10 | M@20 | P@10 | P@20 | M@10 | M@20 | |
GRU4Rec | 17.93 | 30.79 | 7.73 | 8.22 | 9.47 | 10.93 | 5.78 | 5.89 |
NARM | 35.44 | 48.32 | 15.13 | 16.00 | 19.17 | 23.30 | 10.42 | 10.70 |
STAMP | 33.69 | 46.62 | 14.26 | 15.13 | 22.63 | 26.47 | 13.12 | 13.36 |
SR-GNN | 38.42 | 51.26 | 16.89 | 17.78 | 23.41 | 27.57 | 13.45 | 13.72 |
GC-SAN | 38.91 | 51.90 | 17.31 | 18.21 | 19.21 | 23.30 | 10.67 | 10.96 |
FGNN | 37.72 | 50.58 | 15.95 | 16.84 | 20.67 | 25.24 | 10.07 | 10.39 |
GCE-GNN | 41.16 | 54.22 | 18.15 | 19.04 | 28.01 | 33.42 | 15.08 | 15.42 |
S2-DHCN | 40.21 | 53.66 | 17.59 | 18.51 | 26.22 | 31.42 | 14.60 | 15.05 |
Disen-GNN | 40.63 | 53.79 | 17.98 | 18.99 | 25.87 | 30.78 | 15.05 | 15.40 |
HyperS2Rec | 40.52 | 54.13 | 18.09 | 18.91 | 27.26 | 32.91 | 14.98 | 15.39 |
CGSNet | ||||||||
SR-GAL | 41.75 | 55.01 | 18.60 | 19.50 | 33.27 | 39.08 | 17.38 | 17.79 |
模型 | 复杂度 | Diginetica | Tmall | ||
---|---|---|---|---|---|
训练时间/s | 内存/MB | 训练时间/s | 内存/MB | ||
SR-GNN | O(l(nd2+n3)+nd2) | 330 | 2 385 | 152 | 2 139 |
GC-SAN | O(l(nd2+n3)+n2d) | 326 | 2 419 | 152 | 2 381 |
Disen-GNN | O(lc(nd2+n3+nd2+n2d2)+nd2) | 1 121 | 21 549 | 691 | 18 891 |
S2-DHCN | O(l|E|d+b2d2+nd2) | 1 098 | 2 753 | 725 | 2 631 |
HyperS2Rec | O(l|E|d+ln3+nd2) | 844 | 2 355 | 312 | 2 324 |
GCE-GNN | O(ln2d+nkd+nd2) | 600 | 6 917 | 110 | 2 863 |
CGSNet | O(ln2d2+b2d2+ln2d+nkd+nd2) | 763 | 7 463 | 280 | 3 465 |
SR-GAL | O(ln2d+lnd2+nd2) | 370 | 4 529 | 176 | 3 151 |
Tab. 3 Comparison of computational complexity of different models
模型 | 复杂度 | Diginetica | Tmall | ||
---|---|---|---|---|---|
训练时间/s | 内存/MB | 训练时间/s | 内存/MB | ||
SR-GNN | O(l(nd2+n3)+nd2) | 330 | 2 385 | 152 | 2 139 |
GC-SAN | O(l(nd2+n3)+n2d) | 326 | 2 419 | 152 | 2 381 |
Disen-GNN | O(lc(nd2+n3+nd2+n2d2)+nd2) | 1 121 | 21 549 | 691 | 18 891 |
S2-DHCN | O(l|E|d+b2d2+nd2) | 1 098 | 2 753 | 725 | 2 631 |
HyperS2Rec | O(l|E|d+ln3+nd2) | 844 | 2 355 | 312 | 2 324 |
GCE-GNN | O(ln2d+nkd+nd2) | 600 | 6 917 | 110 | 2 863 |
CGSNet | O(ln2d2+b2d2+ln2d+nkd+nd2) | 763 | 7 463 | 280 | 3 465 |
SR-GAL | O(ln2d+lnd2+nd2) | 370 | 4 529 | 176 | 3 151 |
模型 | Diginetica | Tmall | ||||||
---|---|---|---|---|---|---|---|---|
P@10 | P@20 | M@10 | M@20 | P@10 | P@20 | M@10 | M@20 | |
SR-GAL-PC | 40.88 | 54.08 | 17.82 | 18.76 | 28.14 | 33.82 | 14.61 | 15.01 |
SR-GAL-P | 41.44 | 54.59 | 18.33 | 19.24 | 28.72 | 34.34 | 14.74 | 15.14 |
SR-GAL-C | 41.71 | 54.79 | 18.36 | 19.26 | 29.38 | 34.98 | 15.12 | 15.51 |
SR-GAL | 41.75 | 55.01 | 18.60 | 19.50 | 33.27 | 39.08 | 17.38 | 17.79 |
Tab. 4 Performance comparison of different variants of SR-GAL
模型 | Diginetica | Tmall | ||||||
---|---|---|---|---|---|---|---|---|
P@10 | P@20 | M@10 | M@20 | P@10 | P@20 | M@10 | M@20 | |
SR-GAL-PC | 40.88 | 54.08 | 17.82 | 18.76 | 28.14 | 33.82 | 14.61 | 15.01 |
SR-GAL-P | 41.44 | 54.59 | 18.33 | 19.24 | 28.72 | 34.34 | 14.74 | 15.14 |
SR-GAL-C | 41.71 | 54.79 | 18.36 | 19.26 | 29.38 | 34.98 | 15.12 | 15.51 |
SR-GAL | 41.75 | 55.01 | 18.60 | 19.50 | 33.27 | 39.08 | 17.38 | 17.79 |
模型 | Diginetica | Tmall | |||||||
---|---|---|---|---|---|---|---|---|---|
P@10 | P@20 | M@10 | M@20 | P@10 | P@20 | M@10 | M@20 | ||
SR-GNN | w/o | 38.42 | 51.26 | 16.89 | 17.78 | 23.41 | 27.57 | 13.45 | 13.72 |
w | 40.72 | 53.88 | 17.82 | 18.74 | 26.08 | 31.01 | 14.56 | 14.91 | |
GC-SAN | w/o | 38.91 | 51.90 | 17.31 | 18.20 | 19.21 | 23.30 | 10.67 | 10.96 |
w | 40.51 | 53.66 | 17.77 | 18.68 | 23.86 | 28.54 | 13.11 | 13.56 | |
Disen-GNN | w/o | 40.63 | 53.79 | 17.98 | 18.99 | 25.87 | 30.78 | 15.05 | 15.40 |
w | 41.27 | 54.50 | 18.38 | 19.30 | 26.72 | 32.15 | 15.46 | 15.80 |
Tab. 5 Performance comparison of different models before and after combining with auxiliary tasks
模型 | Diginetica | Tmall | |||||||
---|---|---|---|---|---|---|---|---|---|
P@10 | P@20 | M@10 | M@20 | P@10 | P@20 | M@10 | M@20 | ||
SR-GNN | w/o | 38.42 | 51.26 | 16.89 | 17.78 | 23.41 | 27.57 | 13.45 | 13.72 |
w | 40.72 | 53.88 | 17.82 | 18.74 | 26.08 | 31.01 | 14.56 | 14.91 | |
GC-SAN | w/o | 38.91 | 51.90 | 17.31 | 18.20 | 19.21 | 23.30 | 10.67 | 10.96 |
w | 40.51 | 53.66 | 17.77 | 18.68 | 23.86 | 28.54 | 13.11 | 13.56 | |
Disen-GNN | w/o | 40.63 | 53.79 | 17.98 | 18.99 | 25.87 | 30.78 | 15.05 | 15.40 |
w | 41.27 | 54.50 | 18.38 | 19.30 | 26.72 | 32.15 | 15.46 | 15.80 |
模型 | Diginetica | Tmall | |||||
---|---|---|---|---|---|---|---|
训练 时间/s | 内存/MB | 参数量/106 | 训练 时间/s | 内存/MB | 参数量/106 | ||
SR-GNN | w/o | 330 | 2 385 | 4.49 | 152 | 2 319 | 4.25 |
w | 393 | 2 559 | 4.53 | 212 | 2 517 | 4.28 | |
GC-SAN | w/o | 326 | 2 419 | 5.42 | 152 | 2 381 | 5.14 |
w | 381 | 2 591 | 5.46 | 183 | 2 539 | 5.17 | |
Disen-GNN | w/o | 1 121 | 21 549 | 3.56 | 691 | 18 891 | 4.24 |
w | 1 290 | 21 843 | 3.60 | 729 | 19 221 | 4.27 |
Tab. 6 Influence of auxiliary tasks on training time, parameters and memory usage of different models
模型 | Diginetica | Tmall | |||||
---|---|---|---|---|---|---|---|
训练 时间/s | 内存/MB | 参数量/106 | 训练 时间/s | 内存/MB | 参数量/106 | ||
SR-GNN | w/o | 330 | 2 385 | 4.49 | 152 | 2 319 | 4.25 |
w | 393 | 2 559 | 4.53 | 212 | 2 517 | 4.28 | |
GC-SAN | w/o | 326 | 2 419 | 5.42 | 152 | 2 381 | 5.14 |
w | 381 | 2 591 | 5.46 | 183 | 2 539 | 5.17 | |
Disen-GNN | w/o | 1 121 | 21 549 | 3.56 | 691 | 18 891 | 4.24 |
w | 1 290 | 21 843 | 3.60 | 729 | 19 221 | 4.27 |
1 | HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks [EB/OL]. [2022-07-05]. . |
2 | LI J, REN P, CHEN Z, et al. Neural attentive session-based recommendation [C]// Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York: ACM, 2017: 1419-1428. |
3 | LIU Q, ZENG Y, MOKHOSI R, et al. STAMP: short-term attention/memory priority model for session-based recommendation[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2018: 1831-1839. |
4 | WU S, TANG Y, ZHU Y, et al. Session-based recommendation with graph neural networks [C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2019: 346-353. |
5 | LI A, CHENG Z, LIU F, et al. Disentangled graph neural networks for session-based recommendation [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(8): 7870-7882. |
6 | WANG Z, WEI W, CONG G, et al. Global context enhanced graph neural networks for session-based recommendation [C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020: 169-178. |
7 | DING C, ZHAO Z, LI C, et al. Session-based recommendation with hypergraph convolutional networks and sequential information embeddings [J]. Expert Systems with Applications, 2023, 223: No.119875. |
8 | XIA X, YIN H, YU J, et al. Self-supervised hypergraph convolutional networks for session-based recommendation [C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2021: 4503-4511. |
9 | WANG F, LU X, LYU L. CGSNet: contrastive graph self-attention network for session-based recommendation [J]. Knowledge-Based Systems, 2022, 251: No.109282. |
10 | 党伟超,程炳阳,高改梅,等. 基于对比超图转换器的会话推荐[J]. 计算机应用, 2023, 43(12):3683-3688. |
DANG W C, CHENG B Y, GAO G M, et al. Contrastive hypergraph transformer for session-based recommendation [J]. Journal of Computer Applications, 2023, 43(12): 3683-3688. | |
11 | HOU Y, HU B, ZHANG Z, et al. CORE: simple and effective session-based recommendation within consistent representation space [C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 1796-1801. |
12 | TAO Y, GAO M, YU J, et al. Predictive and contrastive: dual-auxiliary learning for recommendation [J]. IEEE Transactions on Computational Social System, 2023, 10(5): 2254-2265. |
13 | ZHOU K, YU H, ZHAO W X, et al. Filter-enhanced MLP is all you need for sequential recommendation [C]// Proceedings of the ACM Web Conference 2022. New York: ACM, 2022: 2388-2399. |
14 | XU C, ZHAO P, LIU Y, et al. Graph contextualized self-attention network for session-based recommendation [C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence. California: IJCAI.org, 2019: 3940-3946. |
15 | YU F, ZHU Y, LIU Q, et al. TAGNN: target attentive graph neural networks for session-based recommendation [C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020: 1921-1924. |
16 | QIU R, LI J, HUANG Z, et al. Rethinking the item order in session-based recommendation with graph neural networks [C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 579-588. |
17 | SHENG Z, ZHANG T, ZHANG Y, et al. Enhanced graph neural network for session-based recommendation [J]. Expert Systems with Applications, 2023, 213(Pt A): No.118887. |
18 | 孙轩宇,史艳翠. 融合项目影响力的图神经网络会话推荐模型[J]. 计算机应用, 2023, 43(12):3689-3696. |
SUN X Y, SHI Y C. Session-based recommendation model by graph neural network fused with item influence [J]. Journal of Computer Applications, 2023, 43(12): 3689-3696. | |
19 | LI H, LUO X, YU Q, et al. Session-based recommendation via contrastive learning on heterogeneous graph [C]// Proceedings of the 2021 IEEE International Conference on Big Data. Piscataway: IEEE, 2021: 1077-1082. |
20 | XIA X, YIN H, YU J, et al. Self-supervised graph co-training for session-based recommendation [C]// Proceedings of the 30th ACM International Conference on Information and Knowledge Management. New York: ACM, 2021: 2180-2190. |
21 | HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. |
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