[Objective] This study proposes a deep learning-based recommendation model for research teams to meet recruitment needs and improve recommendation efficiency. [Methods] Firstly, we applied the self-attention mechanism to learn the semantic representation of teams. Then, we employed the neural collaborative filtering model to study the nonlinear relationship between teams and researchers. Finally, we obtained the degree of fit between teams and individuals as the basis for recommendation. [Results] Compared with the baseline models, the proposed one increased the recommendation accuracy and F1 value by 10.22% and 10.25%, respectively, on public datasets. It performed exceptionally well in real-world recommendation scenarios. [Limitations] The parameter size of the deep learning model is relatively small, leaving room for optimization. [Conclusions] The proposed model can effectively enhance the efficiency of recruiting researchers, helping research service institutions improve their services and satisfy the needs of research teams.
刘成山, 李普国, 汪圳. 一种以科研团队为服务对象的科研人员推荐模型*[J]. 数据分析与知识发现, 2024, 8(3): 132-142.
Liu Chengshan, Li Puguo, Wang Zhen. A Researcher Recommendation Model for Research Teams. Data Analysis and Knowledge Discovery, 2024, 8(3): 132-142.
(Zhang Yujie, Du Yulu, Meng Xiangwu. Research on Group Recommender Systems and Their Applications[J]. Chinese Journal of Computers, 2016, 39(4):745-764.)
[2]
Garcia I, Pajares S, Sebastia L, et al. Preference Elicitation Techniques for Group Recommender Systems[J]. Information Sciences, 2012, 189: 155-175.
doi: 10.1016/j.ins.2011.11.037
[3]
Carvalho L A M C, Macedo H T. Users’ Satisfaction in Recommendation Systems for Groups: An Approach Based on Noncooperative Games[C]// Proceedings of the 22nd International Conference on World Wide Web. 2013: 951-958.
[4]
Liu X, Tian Y, Ye M, et al. Exploring Personal Impact for Group Recommendation[C]// Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012: 674-683.
[5]
Yuan Q, Cong G, Lin C Y. COM: A Generative Model for Group Recommendation[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014: 163-172.
(Zhang Chunjin, Guo Shenghui, Ji Shujuan, et al. Group Recommendation Algorithms Based on Implicit Representation Learning of Multi-attribute Ratings[J]. Data Analysis and Knowledge Discovery, 2020, 4(12): 120-135.)
[7]
Cao D, He X, Miao L, et al. Attentive Group Recommendation[C]// Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018: 645-654.
[8]
Cao D, He X, Miao L, et al. Social-Enhanced Attentive Group Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(3): 1195-1209.
doi: 10.1109/TKDE.69
[9]
Wang H, Li Y, Frimpong F. Group Recommendation via Self-attention and Collaborative Metric Learning Model[J]. IEEE Access, 2019, 7: 164844-164855.
doi: 10.1109/Access.6287639
(Liu Haohan, Ren Hongrun, He Huaiqing. Group Recommendation Method Based on Self-attention Mechanism[J]. Application Research of Computers, 2020, 37(12):3572-3577.)
(Zhang Yaokun, Wu Rui, Zong Jinxing. Evolutionary Game Analysis of Scientific Research Collaborative Behavior in Academic Social Networks Using System Dynamics[J]. Journal of Modern Information, 2022, 42(7): 68-80.)
doi: 10.3969/j.issn.1008-0821.2022.07.006
(Xiong Huixiang, Yang Xueping, Jiang Wuxuan, et al. Scholars Recommend Research Based on Academic Competence and Collaborative Networks[J]. Information Science, 2019, 37(5): 71-78.)
(Xiong Huixiang, Li Xiaomin, Du Jin. Scholar Recommendation Research Based on Academic Keywords and Co-citation[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(7): 725-733.)
(Chen Weijing, Zheng Ying. Mining Potential Cooperative Relationships Based on the Author Keyword Coupling Analysis[J]. Journal of Intelligence, 2013, 32(5):127-131.)
(Zhang Jinzhu, Han Tao, Wang Xiaomei. Co-authorship Prediction in the Author-keyword Bipartite Networks[J]. Library and Information Service, 2016, 60(21): 74-80.)
doi: 10.13266/j.issn.0252-3116.2016.21.010
(Liu Ping, Zheng Kailun, Zou De’an. Research on Cooperative Recommendation Based on LDA Model[J]. Information Studies: Theory & Application, 2015, 38(9): 79-85.)
(Pu Shanshan. Expert Recommendation Model in Scientific and Technical Collaboration Based on Complementary Knowledge[J]. Information Studies: Theory & Application, 2018, 41(8): 96-101.)
[18]
Tang J, Wu S, Sun J, et al. Cross-domain Collaboration Recommendation[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012: 1285-1293.
(Li Zhong, Han Hongqi, Wu Guangyin, et al. Academic Collaboration Recommendation Based on Sparse Distributed Representation[J]. Information Science, 2019, 37(6): 113-118.)
(Yu Chuanming, Lin Aochen, Zhong Yunci, et al. Scientific Collaboration Recommendation Based on Network Embedding[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(5): 500-511.)
(Lin Yuan, Liu Haifeng, Wang Hailong, et al. Potential Cooperation Opportunities Exploration Between Scholars Based on Presentation Learning[J]. Journal of Intelligence, 2019, 38(5): 65-70.)
(Yang Xinjun. Research and Implementation of Research Project Team Recommendation System Based on Expression Learning[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.)
(Hong Shaowen, Tang Yong, Yuan Chengzhe, et al. Recommendation Model of Virtual Team Members in Social Network[J]. Journal of Guangxi University(Natural Science Edition), 2014, 39(5):1109-1115.)
(Yuan Chengzhe, Zeng Biqing, Tang Yong, et al. Multi-Faceted Team Recommendation Model for Academic Social Networks[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(2): 201-209.)
doi: 10.3778/j.issn.1673-9418.1506047
[25]
Vaswani A, Shazeer N, Parmar N, et al. Attention is All You Need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.
[26]
He X, Liao L, Zhang H, et al. Neural Collaborative Filtering[C]// Proceedings of the 26th International Conference on World Wide Web. 2017: 173-182.
[27]
De Brébisson A, Simon É, Auvolat A, et al. Artificial Neural Networks Applied to Taxi Destination Prediction[OL]. arXiv Preprint, arXiv:1508.00021.
[28]
Grover A, Leskovec J. node2vec: Scalable Feature Learning for Networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 855-864.
[29]
Zhang S, Tay Y, Yao L, et al. Next Item Recommendation with Self-Attentive Metric Learning[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019.
[30]
Xu Q, Qiu L, Lin R, et al. An Improved Community Detection Algorithm via Fusing Topology and Attribute Information[C]// Proceedings of the 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2021: 1069-1074.
