Page 101 - 《软件学报》2026年第5期

1980 软件学报 2026 年第 37 卷第 5 期

[25] Oh S, Lei Z, Lee WC, Yen J. Recommending missing citations for newly granted patents. In: Proc. of the 2014 Int’l Conf. on Data
Science and Advanced Analytics (DSAA). Shanghai: IEEE, 2014. 442–448. [doi: 10.1109/DSAA.2014.7058110]
[26] Trappey AJC, Trappey CV, Wu CY, Fan CY, Lin YL. Intelligent recommendation methodology and system for patent search. In: Proc. of
the 16th IEEE Int’l Conf. on Computer Supported Cooperative Work in Design (CSCWD). Wuhan: IEEE, 2012. 172–178. [doi: 10.1109/
CSCWD.2012.6221815]
[27] Mahdabi P, Crestani F. Query-driven mining of citation networks for patent citation retrieval and recommendation. In: Proc. of the 23rd
ACM Int’l Conf. on Information and Knowledge Management. Shanghai: ACM, 2014. 1659–1668. [doi: 10.1145/2661829.2661899]
[28] He Q, Spangler WS, He B, Chen Y, Kato L. Prospective client driven technology recommendation. In: Proc. of the 2012 Annual SRII
Global Conf. San Jose: IEEE, 2012. 110–119. [doi: 10.1109/SRII.2012.23]
[29] Fu TY, Lei Z, Lee WC. Patent citation recommendation for examiners. In: Proc. of the 2015 IEEE Int’l Conf. on Data Mining. Atlantic
City: IEEE, 2015. 751–756. [doi: 10.1109/ICDM.2015.151]
[30] Chen YL, Chiu YT. An IPC-based vector space model for patent retrieval. Information Processing & Management, 2011, 47(3): 309–322.
[doi: 10.1016/j.ipm.2010.06.001]
[31] Krestel R, Smyth P. Recommending patents based on latent topics. In: Proc. of the 7th ACM Conf. on Recommender Systems. Hong
Kong: ACM, 2013. 395–398. [doi: 10.1145/2507157.2507232]
[32] Liu SH, Liao HL, Pi SM, Hu JW. Development of a patent retrieval and analysis platform—A hybrid approach. Expert Systems with
Applications, 2011, 38(6): 7864–7868. [doi: 10.1016/j.eswa.2010.12.114]
[33] Huang ZH, Zhang JW, Tian CQ, Sun SL, Xiang Y. Survey on learning-to-rank based recommendation algorithms. Ruan Jian Xue
Bao/Journal of Software, 2016, 27(3): 691–713 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4948.htm [doi: 10.
13328/j.cnki.jos.004948]
[34] Yang H, Wu X, Qiu ZP, Zheng YF, Chen X. Distributional fairness-aware recommendation. ACM Trans. on Information Systems, 2024,
42(5): 131. [doi: 10.1145/3652854]
[35] He RN, McAuley J. VBPR: Visual Bayesian personalized ranking from implicit feedback. In: Proc. of the 30th AAAI Conf. on Artificial
Intelligence. Phoenix: AAAI press, 2016. 144–150. [doi: 10.1609/aaai.v30i1.9973]
[36] Liu Q, Wu S, Wang L. DeepStyle: Learning user preferences for visual recommendation. In: Proc. of the 40th Int’l ACM SIGIR Conf. on
Research and Development in Information Retrieval. Shinjuku: ACM, 2017. 841–844. [doi: 10.1145/3077136.3080658]
[37] Chen X, Chen HX, Xu HT, Zhang YF, Cao YX, Qin Z, Zha HY. Personalized fashion recommendation with visual explanations based on
multimodal attention network: Towards visually explainable recommendation. In: Proc. of the 42nd Int’l ACM SIGIR Conf. on Research
and Development in Information Retrieval. Paris: ACM, 2019. 765–774. [doi: 10.1145/3331184.3331254]
[38] Liu F, Cheng ZY, Sun CC, Wang YL, Nie LQ, Kankanhalli M. User diverse preference modeling by multimodal attentive metric
learning. In: Proc. of the 27th ACM Int’l Conf. on Multimedia. Nice: ACM, 2019. 1526–1534. [doi: 10.1145/3343031.3350953]
[39] Zhao DD, Xu H, Peng SY, Zhou JW. Privacy-preserving graph neural network recommendation system based on negative database. Ruan
Jian Xue Bao/Journal of Software, 2024, 35(8): 3698–3720 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7124.
htm [doi: 10.13328/j.cnki.jos.007124]
[40] Gao C, Zheng Y, Wang WJ, Feng FL, He XN, Li Y. Causal inference in recommender systems: A survey and future directions. ACM
Trans. on Information Systems, 2024, 42(4): 88. [doi: 10.1145/3639048]
[41] Wu SW, Sun F, Zhang WT, Xie X, Cui B. Graph neural networks in recommender systems: A survey. ACM Computing Surveys, 2022,
55(5): 97. [doi: 10.1145/3535101]
[42] Xu BB, Cen KT, Huang JJ, Shen HW, Cheng XQ. A survey on graph convolutional neural network. Chinese Journal of Computers, 2020,
43(5): 755–780 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2020.00755]
[43] Wei YW, Wang X, Nie LQ, He XN, Hong RC, Chua TS. MMGCN: Multi-modal graph convolution network for personalized
recommendation of micro-video. In: Proc. of the 27th ACM Int’l Conf. on Multimedia. Nice: ACM, 2019. 1437–1445. [doi: 10.1145/
3343031.3351034]
[44] Wang QF, Wei YW, Yin JH, Wu JL, Song XM, Nie LQ. DualGNN: Dual graph neural network for multimedia recommendation. IEEE
Trans. on Multimedia, 2023, 25: 1074–1084. [doi: 10.1109/TMM.2021.3138298]
[45] Wei YW, Wang X, Nie LQ, He XN, Chua TS. Graph-refined convolutional network for multimedia recommendation with implicit
feedback. In: Proc. of the 28th ACM Int’l Conf. on Multimedia. Seattle: ACM, 2020. 3541–3549. [doi: 10.1145/3394171.3413556]
[46] Zhang JH, Zhu YQ, Liu Q, Wu S, Wang SH, Wang L. Mining latent structures for multimedia recommendation. In: Proc. of the 29th
ACM Int’l Conf. on Multimedia. ACM, 2021. 3872–3880. [doi: 10.1145/3474085.3475259]
[47] Zhou X, Shen ZQ. A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation. In: Proc. of the 31st