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P. 100
赖培源 等: 基于多模态异质图网络的专利推荐算法 1979
[4] Galasso A, Schankerman M, Serrano CJ. Trading and enforcing patent rights. The RAND Journal of Economics, 2013, 44(2): 275–312.
[doi: 10.1111/1756-2171.12020]
[5] Chen J, Chen JL, Zhao S, Zhang YP, Tang J. Exploiting word embedding for heterogeneous topic model towards patent recommendation.
Scientometrics, 2020, 125(3): 2091–2108. [doi: 10.1007/s11192-020-03666-4]
[6] Ye L, Zhang T, Cao XZ, Hu SL, Zeng G. Mapping the landscape of university technology flows in China using patent assignment data.
Humanities and Social Sciences Communications, 2024, 11(1): 473. [doi: 10.1057/s41599-024-02982-x]
[7] Muscio A. What drives the university use of technology transfer offices? Evidence from Italy. The Journal of Technology Transfer, 2010,
35(2): 181–202. [doi: 10.1007/s10961-009-9121-7]
[8] Trappey AJC, Trappey CV, Wu CY, Fan CY, Lin YL. Intelligent patent recommendation system for innovative design collaboration.
Journal of Network and Computer Applications, 2013, 36(6): 1441–1450. [doi: 10.1016/j.jnca.2013.02.035]
[9] Ji X, Gu XJ, Dai F, Chen JX, Le CY. Patent collaborative filtering recommendation approach based on patent similarity. In: Proc. of the
8th Int’l Conf. on Fuzzy Systems and Knowledge Discovery (FSKD). Shanghai: IEEE, 2011. 1699–1703. [doi: 10.1109/FSKD.2011.
6019821]
[10] Yoon J, Seo W, Coh BY, Song I, Lee JM. Identifying product opportunities using collaborative filtering-based patent analysis. Computers
& Industrial Engineering, 2017, 107: 376–387. [doi: 10.1016/j.cie.2016.04.009]
[11] Trappey A, Trappey CV, Hsieh A. An intelligent patent recommender adopting machine learning approach for natural language
processing: A case study for smart machinery technology mining. Technological Forecasting and Social Change, 2021, 164: 120511. [doi:
10.1016/j.techfore.2020.120511]
[12] Rui XH, Min D. HIM-PRS: A patent recommendation system based on hierarchical index-based MapReduce framework. In: Proc. of the
Advances in Computer Science and Ubiquitous Computing: CSA-CUTE 2016. Singapore: Springer, 2017. 843–848. [doi: 10.1007/978-
981-10-3023-9_130]
[13] Liu XJ, Wan YY, Liu XB, Zhang JH. A Patent recommendation algorithm based on topic classification and semantic similarity. In: Proc.
of the 2021 Int’l Conf. on Wireless Communications and Smart Grid (ICWCSG). Hangzhou: IEEE, 2021. 289–292. [doi: 10.1109/
ICWCSG53609.2021.00063]
[14] Wang Q, Du W, Ma J, Liao XW. Recommendation mechanism for patent trading empowered by heterogeneous information networks. Int’l
Journal of Electronic Commerce, 2019, 23(2): 147–178. [doi: 10.1080/10864415.2018.1564549]
[15] Du W, Wang YB, Xu W, Ma J. A personalized recommendation system for high-quality patent trading by leveraging hybrid patent
analysis. Scientometrics, 2021, 126(12): 9369–9391. [doi: 10.1007/s11192-021-04180-x]
[16] Oh S, Lei Z, Lee WC, Mitra P, Yen J. CV-PCR: A context-guided value-driven framework for patent citation recommendation. In: Proc.
of the 22nd ACM Int’l Conf. on Information & Knowledge Management. San Francisco: ACM, 2013. 2291–2296. [doi: 10.1145/2505515.
2505659]
[17] Deng WW, Ma J. A knowledge graph approach for recommending patents to companies. Electronic Commerce Research, 2022, 22(4):
1435–1466. [doi: 10.1007/s10660-021-09471-2]
[18] Lee J, Sohn SY. Recommendation system for technology convergence opportunities based on self-supervised representation learning.
Scientometrics, 2021, 126(1): 1–25. [doi: 10.1007/s11192-020-03731-y]
[19] Chen YW, Zhu PH, Ma J, Huang XM, Qin J. A trust-enhanced patent recommendation approach to university-industry technology
transfer. ACM SIGMIS Database: The Database for Advances in Information Systems, 2024, 55(1): 35–55. [doi: 10.1145/3645057.
3645061]
[20] Liu ZB, Zhang YX, Deng WW, Ma J, Fan X. A deep learning method for recommending university patents to industrial clusters by
common technological needs mining. Scientometrics, 2024, 129(6): 3089–3113. [doi: 10.1007/s11192-024-05052-w]
[21] Du W, Jiang GR, Xu W, Ma J. Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-
term memory network (KBiLSTM). Journal of Information Science, 2023, 49(3): 814–830. [doi: 10.1177/01655515211023937]
[22] Liu Q, He YY, Lian DF, Zheng Z, Xu T, Liu C, Chen EH. UniMEL: A unified framework for multimodal entity linking with large
language models. arXiv:2407.16160, 2024.
[23] Chen BY, Huang L, Wang CD, Jing LP. Explicit and implicit feedback based collaborative filtering algorithm. Ruan Jian Xue
Bao/Journal of Software, 2020, 31(3): 794–805 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5897.htm [doi: 10.
13328/j.cnki.jos.005897]
[24] Wang K, Wang Y, Liu JY, Deng JZ. Graph convolutional collaborative filtering recommendation algorithm based on Rényi differential
privacy. Ruan Jian Xue Bao/Journal of Software, 2025, 36(3): 1202–1217 (in Chinese with English abstract). http://www.jos.org.cn/1000-
9825/7165.htm [doi: 10.13328/j.cnki.jos.007165]

