Page 102 - 《软件学报》2026年第5期
P. 102

赖培源 等: 基于多模态异质图网络的专利推荐算法                                                        1981


                     ACM Int’l Conf. on Multimedia. Ottawa: ACM, 2023. 935–943. [doi: 10.1145/3581783.3611943]
                 [48]   Hu HC, Guo W, Liu Y, Kan MY. Adaptive multi-modalities fusion in sequential recommendation systems. In: Proc. of the 32nd ACM Int’l
                     Conf. on Information and Knowledge Management. Birmingham: ACM, 2023. 843–853. [doi: 10.1145/3583780.3614775]
                 [49]   Zhou HY, Zhou X, Zhang LZ, Shen ZQ. Enhancing dyadic relations with homogeneous graphs for multimodal recommendation. In: Proc.
                     of the 26th European Conf. on Artificial Intelligence. Nice: IOS Press, 2023. 3123–3130. [doi: 10.3233/FAIA230631]
                 [50]   Chen YK, Sun TH, Ma YH, Zou HH. Multifactorial modality fusion network for multimodal recommendation. Applied Intelligence,
                     2025, 55(2): 139. [doi: 10.1007/S10489-024-06038-0]
                 [51]   Liu YH, Ott M, Goyal N, Du JF, Joshi M, Chen DQ, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. RoBERTa: A robustly optimized
                     BERT pretraining approach. arXiv:1907.11692, 2019.
                 [52]   Dosovitskiy  A,  Beyer  L,  Kolesnikov  A,  Weissenborn  D,  Zhai  XH,  Unterthiner  T,  Dehghani  M,  Minderer  M,  Heigold  G,  Gelly  S,
                     Uszkoreit J, Houlsby N. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929, 2021.
                 [53]   Yu PH, Tan ZY, Lu GM, Bao BK. Multi-view graph convolutional network for multimedia recommendation. In: Proc. of the 31st ACM
                     Int’l Conf. on Multimedia. Ottawa: ACM, 2023. 6576–6585. [doi: 10.1145/3581783.3613915]
                 [54]   Zhou  X,  Zhou  HY,  Liu  Y,  Zeng  ZW,  Miao  CY,  Wang  PW,  You  Y,  Jiang  FJ.  Bootstrap  latent  representations  for  multi-modal
                     recommendation. In: Proc. of the 2023 ACM Web Conf. Austin: ACM, 2023. 845–854. [doi: 10.1145/3543507.3583251]
                 [55]   Zhong SS, Huang ZZ, Li DF, Wen WS, Qin JH, Lin L. Mirror gradient: Towards robust multimodal recommender systems via exploring
                     flat local minima. In: Proc. of the 2024 ACM Web Conf. Singapore: ACM, 2024. 3700–3711. [doi: 10.1145/3589334.3645553]
                 [56]   Arora S, Liang YY, Ma TY. A simple but tough-to-beat baseline for sentence embeddings. In: Proc. of the 2017 Int’l Conf. on Learning
                     Representations. OpenReview.net, 2017.
                 [57]   He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc. of the 2016 IEEE Conf. on Computer Vision
                     and Pattern Recognition. Las Vegas: IEEE, 2016. 770–778. [doi: 10.1109/CVPR.2016.90]
                 [58]   Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using siamese BERT-networks. arXiv:1908.10084, 2019.
                 [59]   Jia YQ, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: Convolutional architecture for fast
                     feature  embedding.  In:  Proc.  of  the  22nd  ACM  Int’l  Conf.  on  Multimedia.  Orlando:  ACM,  2014.  675–678.  [doi:  10.1145/2647868.
                     2654889]

                 附中文参考文献
                 [23]   陈碧毅, 黄玲, 王昌栋, 景丽萍. 融合显式反馈与隐式反馈的协同过滤推荐算法. 软件学报, 2020, 31(3): 794–805. http://www.jos.org.
                     cn/1000-9825/5897.htm [doi: 10.13328/j.cnki.jos.005897]
                 [24]   王锟, 王永, 刘金源, 邓江洲. 基于  Rényi 差分隐私的图卷积协同过滤推荐算法. 软件学报, 2025, 36(3): 1202–1217. http://www.jos.
                     org.cn/1000-9825/7165.htm [doi: 10.13328/j.cnki.jos.007165]
                 [33]   黄震华, 张佳雯, 田春岐, 孙圣力, 向阳. 基于排序学习的推荐算法研究综述. 软件学报, 2016, 27(3): 691–713. http://www.jos.org.cn/
                     1000-9825/4948.htm [doi: 10.13328/j.cnki.jos.004948]
                 [39]   赵冬冬, 徐虎, 彭思芸, 周俊伟. 基于负数据库的隐私保护图神经网络推荐系统. 软件学报, 2024, 35(8): 3698–3720. http://www.jos.
                     org.cn/1000-9825/7124.htm [doi: 10.13328/j.cnki.jos.007124]
                 [42]   徐冰冰, 岑科廷, 黄俊杰, 沈华伟, 程学旗. 图卷积神经网络综述. 计算机学报, 2020, 43(5): 755–780. [doi: 10.11897/SP.J.1016.
                     2020.00755]

                 作者简介
                 赖培源, 博士, 教授, CCF  专业会员, 主要研究领域为数据挖掘, 推荐算法, 知识图谱, 语义表征.
                 卢伊虹, 硕士生, CCF  学生会员, 主要研究领域为图机器学习, 推荐算法, 数据挖掘.
                 廖德章, 工程师, 主要研究领域为数据挖掘, 推荐算法.
                 王昌栋, 博士, 教授, CCF  杰出会员, 主要研究领域为数据聚类, 网络分析, 推荐算法.
                 戴青云, 博士, 教授, CCF  专业会员, 主要研究领域为知识产权大数据, 人工智能.
                 赖剑煌, 博士, 教授, CCF  杰出会员, 主要研究领域为机器学习, 数据挖掘.
   97   98   99   100   101   102   103   104   105   106   107