Page 219 - 《软件学报》2026年第2期
P. 219

698                                                        软件学报  2026  年第  37  卷第  2  期


                     Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 477.
                  [5]   Lan  ZZ,  Chen  MD,  Goodman  S,  Gimpel  K,  Sharma  P,  Soricut  R.  ALBERT:  A  lite  BERT  for  self-supervised  learning  of  language
                     representations. In: Proc. of the 8th Int’l Conf. on Learning Representations. 2020.
                  [6]   van den Oord A, Li YZ, Vinyals O. Representation learning with contrastive predictive coding. arXiv:1807.03748, 2018.
                  [7]   Qiu JZ, Chen QB, Dong YX, Zhang J, Yang HX, Ding M, Wang KS, Tang J. GCC: Graph contrastive coding for graph neural network
                     pre-training. In: Proc. of the 26th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. ACM, 2020. 1150–1160. [doi: 10.
                     1145/3394486.3403168]
                  [8]   Jin W, Derr T, Liu HC, Wang YQ, Wang SH, Liu ZT, Tang JL. Self-supervised learning on graphs: Deep insights and new direction.
                     arXiv:2006.10141, 2020.
                  [9]   You YN, Chen TL, Sui YD, Chen T, Wang ZY, Shen Y. Graph contrastive learning with augmentations. In: Proc. of the 34th Int’l Conf.
                     on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 488.
                 [10]   Wu JC, Wang X, Feng FL, He XN, Chen L, Lian JX, Xie X. Self-supervised graph learning for recommendation. In: Proc. of the 44th Int’l
                     ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2021. 726–735. [doi: 10.1145/3404835.3462862]
                 [11]   Zhang JW, Gao M, Yu JL, Guo L, Li JD, Yin HZ. Double-scale self-supervised hypergraph learning for group recommendation. In: Proc.
                     of the 30th ACM Int’l Conf. on Information & Knowledge Management. Queensland: ACM, 2021. 2557–2567. [doi: 10.1145/3459637.
                     3482426]
                 [12]   Yu JL, Yin HZ, Gao M, Xia X, Zhang XL, Viet Hung NQ. Socially-aware self-supervised tri-training for recommendation. In: Proc. of
                     the 27th ACM SIGKDD Conf. on Knowledge Discovery & Data Mining. ACM, 2021. 2084–2092. [doi: 10.1145/3447548.3467340]
                 [13]   Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618,
                     2012.
                 [14]   Wu ZH, Pan SR, Chen FW, Long GD, Zhang CQ, Yu PS. A comprehensive survey on graph neural networks. IEEE Trans. on Neural
                     Networks and Learning Systems, 2021, 32(1): 4–24. [doi: 10.1109/TNNLS.2020.2978386]
                 [15]   Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: Proc. of the 5th Int’l Conf. on Learning
                     Representations. 2017.
                 [16]   van den Berg R, Kipf TN, Welling M. Graph convolutional matrix completion. arXiv:1706.02263, 2017.
                 [17]   Zhang JN, Shi XJ, Zhao SL, King I. STAR-GCN: Stacked and reconstructed graph convolutional networks for recommender systems. In:
                     Proc. of the 28th Int’l Joint Conf. on Artificial Intelligence. 2019. 4264–4270. [doi: 10.24963/ijcai.2019/592]
                 [18]   Wang X, He XN, Wang M, Feng FL, Chua TS. Neural graph collaborative filtering. In: Proc. of the 42nd Int’l ACM SIGIR Conf. on
                     Research and Development in Information Retrieval. Paris: ACM, 2019. 165–174. [doi: 10.1145/3331184.3331267]
                 [19]   He  XN,  Deng  K,  Wang  X,  Li  Y,  Zhang  YD,  Wang  M.  LightGCN:  Simplifying  and  powering  graph  convolution  network  for
                     recommendation. In: Proc. of the 43rd Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2020.
                     639–648. [doi: 10.1145/3397271.3401063]
                 [20]   Wu S, Tang YY, Zhu YQ, Wang L, Xie X, Tan TN. Session-based recommendation with graph neural networks. In: Proc. of the 33rd
                     AAAI Conf. on Artificial Intelligence. Honolulu: AAAI, 2019. 346–353. [doi: 10.1609/aaai.v33i01.3301346]
                 [21]   Wu  L,  Li  JW,  Sun  PJ,  Hong  RC,  Ge  Y,  Wang  M.  DiffNet++:  A  neural  influence  and  interest  diffusion  network  for  social
                     recommendation. IEEE Trans. on Knowledge and Data Engineering, 2022, 34(10): 4753–4766. [doi: 10.1109/TKDE.2020.3048414]
                 [22]   Yu JL, Yin HZ, Li JD, Wang QY, Hung NQV, Zhang XL. Self-supervised multi-channel hypergraph convolutional network for social
                     recommendation. In: Proc. of the 2021 Web Conf. Ljubljana: ACM, 2021. 413–424. [doi: 10.1145/3442381.3449844]
                 [23]   Oldale A, Oldale J, van Reenen J, Campbell M. Collaborative filtering: US, 2004054572(A1). 2004-03-18.
                 [24]   Yin HZ, Zhou XF, Cui B, Wang H, Zheng K, Nguyen QVH. Adapting to user interest drift for POI recommendation. IEEE Trans. on
                     Knowledge and Data Engineering, 2016, 28(10): 2566–2581. [doi: 10.1109/TKDE.2016.2580511]
                 [25]   Huang C, Xu HC, Xu Y, Dai P, Xia LH, Lu MY, Bo LF, Xing H, Lai XP, Ye YF. Knowledge-aware coupled graph neural network for
                     social recommendation. In: Proc. of the 35th AAAI Conf. on Artificial Intelligence. AAAI, 2021. 4115–4122. [doi: 10.1609/aaai.v35i5.
                     16533]
                 [26]   Yang LW, Liu ZW, Dou YT, Ma J, Yu PS. ConsisRec: Enhancing GNN for social recommendation via consistent neighbor aggregation.
                     In: Proc. of the 44th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2021. 2141–2145. [doi: 10.
                     1145/3404835.3463028]
                 [27]   Fan WQ, Ma Y, Li Q, He Y, Zhao E, Tang JL, Yin DW. Graph neural networks for social recommendation. In: Proc. of the 2019 World
                     Wide Web Conf. San Francisco: ACM, 2019. 417–426. [doi: 10.1145/3308558.3313488]
                 [28]   Song LQ, Bi Y, Yao MQ, Wu ZY, Wang JM, Xiao J. DREAM: A dynamic relation-aware model for social recommendation. In: Proc. of
   214   215   216   217   218   219   220   221   222   223   224