1 |
SHI L, ZHANG Y, CHENG J, et al. Skeleton-based action recognition with directed graph neural networks [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 7912-7921. 10.1109/cvpr.2019.00810
|
2 |
GOMES J, RAMSUNDAR B, FEINBERG E N, et al. Atomic convolutional networks for predicting protein-ligand binding affinity[EB/OL]. [2022-05-10]. .
|
3 |
MA T, XIAO C, ZHOU J, et al. Drug similarity integration through attentive multi-view graph auto-encoders[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: IJCAI, 2018: 3477-3483. 10.24963/ijcai.2018/483
|
4 |
YU B, YIN H, ZHU Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: IJCAI, 2018: 3634-3640. 10.24963/ijcai.2018/505
|
5 |
KOSARAJU V, SADEGHIAN A, MARTÍN-MARTÍN R, et al. Social-bigat: multimodal trajectory forecasting using bicycle-gan and graph attention networks[C]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. [S.l.]: NIPS, 2019: 137-146.
|
6 |
BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral networks and locally connected networks on graphs[EB/OL]. (2014-04-21) [2022-07-10]. . 10.1017/cbo9780511761942.003
|
7 |
KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL]. [2022-06-02]. . 10.48550/arXiv.1609.02907
|
8 |
LI Y, ZEMEL R, BROCKSCHMIDT M, et al. Gated graph sequence neural networks [EB/OL]. [2022-03-26]. .
|
9 |
VELIKOVI P, CUCURULL G, CASANOVA A, et al. Graph attention networks [EB/OL]. [2022-03-28]. .
|
10 |
LEE J B, ROSSI R, KONG X. Graph classification using structural attention [C]// Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2018: 1666-1674. 10.1145/3219819.3219980
|
11 |
刘鑫,梅红岩,王嘉豪,等.图神经网络推荐方法研究[J]. 计算机工程与应用, 2022, 58(10): 41-49. 10.3778/j.issn.1002-8331.2110-0345
|
|
LIU X, MEI H Y, WANG J H, et al. Research on graph neural network recommendation method[J]. Computer Engineering and Applications, 2022, 58(10): 41-49. 10.3778/j.issn.1002-8331.2110-0345
|
12 |
SHANI G, HECKERMAN D, BRAFMAN R I, et al. An MDP-based recommender system [J]. Journal of Machine Learning Research, 2005, 6: 1265-1295.
|
13 |
RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L. Factorizing personalized Markov chains for next-basket recommendation[C]// Proceedings of the 19th International Conference on World Wide Web. New York: ACM, 2010: 811-820. 10.1145/1772690.1772773
|
14 |
DAVIDSON J, LIEBALD B, LIU J, et al. The YouTube video recommendation system[C]// Proceedings of the 2010 4th ACM Conference on Recommender Systems. New York: ACM, 2010: 293-296. 10.1145/1864708.1864770
|
15 |
HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[EB/OL]. [2022-04-15]. .
|
16 |
TAN Y K, XU X, LIU Y. Improved recurrent neural networks for session-based recommendations[C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York: ACM, 2016: 17-22. 10.1145/2988450.2988452
|
17 |
LI J, REN P, CHEN Z, et al. Neural attentive session-based recommendation [C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. New York: ACM, 2017: 1419-1428. 10.1145/3132847.3132926
|
18 |
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 & Data Mining. New York: ACM, 2018: 1831-1839. 10.1145/3219819.3219950
|
19 |
WU S, TANG Y, ZHU Y, et al. Session-based recommendation with graph neural networks [EB/OL]. [2022-11-01].. 10.1609/aaai.v33i01.3301346
|
20 |
SONG W, XIAO Z, WANG Y, et al. Session-based social recommendation via dynamic graph attention networks[C]// Proceedings of the 12th ACM International Conference on Web Search and Data Mining. New York: ACM, 2019: 555-563. 10.1145/3289600.3290989
|
21 |
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. 10.1145/3397271.3401319
|
22 |
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. PaloAlto, CA: AAAI Press, 2019: 3940-3946. 10.24963/ijcai.2019/547
|
23 |
XIAN X, FANG L, SUN S. ReGNN: a repeat aware graph neural network for session-based recommendations[J]. IEEE Access, 2020, 8: 98518-98525. 10.1109/ACCESS.2020.2997722
|
24 |
CHEN T, WONG R C W. Handling information loss of graph neural networks for session-based recommendation[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2020: 1172-1180. 10.1145/3394486.3403170
|
25 |
FANG J. Session-based Recommendation with self-attention networks[EB/OL]. [2022-04-28]. .
|
26 |
南宁, 杨程屹, 武志昊. 基于多图神经网络的会话感知推荐模型[J]. 计算机应用, 2021, 41(2): 330-336. 10.11772/j.issn.1001-9081.2020060805
|
|
NAN N, YANG C Y, WU Z H. Multi-graph neural network-based session perception recommendation model[J]. Journal of Computer Applications, 2021, 41(2): 330-336. 10.11772/j.issn.1001-9081.2020060805
|
27 |
任俊伟, 曾诚, 肖丝雨, 等. 基于会话的多粒度图神经网络推荐模型[J]. 计算机应用, 2021, 41(11): 3164-3170. 10.11772/j.issn.1001-9081.2021010060
|
|
REN J W, ZENG C, XIAO S Y, et al. Session-based recommendation model of multi-granular graph neural network[J]. Journal of Computer Applications, 2021, 41(11): 3164-3170. 10.11772/j.issn.1001-9081.2021010060
|
28 |
黄震华, 林小龙, 孙圣力, 等. 会话场景下基于特征增强的图神经推荐方法[J]. 计算机学报, 2022, 45(4): 766-780. 10.11897/SP.J.1016.2022.00766
|
|
HUANG Z H, LIN X L, SUN S L, et al. Feature augmentation based graph neural recommendation method in session scenarios[J]. Chinese Journal of Computers, 2022, 45(4): 766-780. 10.11897/SP.J.1016.2022.00766
|
29 |
闫昭,项欣光,李泽超.基于交互序列商品相关性建模的图卷积会话推荐[J]. 中国科学: 信息科学, 2022, 52(6): 1069-1082.
|
|
YAN Z, XIANG X G, LI Z C. Graph convolution session recommendation based on interactive sequence commodity correlation modeling[J]. SCIENTIA SINICA Informationis, 2022, 52(6): 1069-1082.
|
30 |
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. 10.1145/3477495.3531955
|
31 |
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 4700-4708. 10.1109/cvpr.2017.243
|