A Review and Analysis of Recommendation Systems in Collaboration Networks
Resumo
Os sistemas de recomendação são amplamente utilizados para fornecer sugestões personalizadas em diversos domínios. Em redes de colaboração científica, esses sistemas ajudam a identificar potenciais colaboradores de pesquisa, analisando dados da rede e atributos dos pesquisadores. Este estudo visa resumir as conclusões da revisão e analisar as pesquisas publicadas sobre sistemas de recomendação utilizados em redes de colaboração científica. O estudo oferece uma compreensão abrangente do uso de sistemas de recomendação em redes de colaboração científica, destacando padrões, tendências, limitações e lacunas de pesquisa neste campo.
Palavras-chave:
Recommender Systems, Collaboration Networks, Scientific Research, Systematic Review
Referências
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R. Albert and A. Barabasi. Statistical mechanics of complex networks. Reviews of Modern Physics, 74:47–97, 2002. DOI: 10.1103/revmodphys.74.47.
Karim Alinani, Guojun Wang, Annadil Alinani, Dua Hussain Narejo, and Mumbere Muyisa Forrest. Aggregating author profiles from multiple publisher networks to build author knowledge graph. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (Smart-World/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 1414–1421, 2018. DOI: 10.1109/SmartWorld.2018.00245.
Masataka Araki, Marie Katsurai, Ikki Ohmukai, and Hideaki Takeda. Interdisciplinary collaborator recommendation based on research content similarity. IEICE Transactions on Information and Systems, E100.D, 10 2016. DOI: 10.1587/transinf.2016DAP0030.
Marcus Barbosa, Walter Nakamura, Pedro Valle, Guilherme Guerino, Alice Finger, Gabriel Machado Lunardi, and Williamson Silva. Ux of chatbots: An exploratory study on acceptance of user experience evaluation methods. In ICEIS, volume 2, pages 355–363, 2022.
J. Beel, B. Gipp, S. Langer, and C. Breitinger. Research-paper recommender systems: a literature survey. International Journal on Digital Libraries, 17:305–338, 2015. DOI: 10.1007/s00799-015-0156-0.
Paweena Chaiwanarom and Chidchanok Lursinsap. Collaborator recommendation in inter-disciplinary computer science using degrees of collaborative forces, temporal evolution of research interest, and comparative seniority status. Know.-Based Syst., 75(C):161–172, feb 2015. ISSN 0950-7051. DOI: 10.1016/j.knosys.2014.11.029.
F. Cheng, Y. Huang, D. Tsaih, and C. Wu. Trend analysis of co-authorship network in library hi tech. Library Hi Tech, 37:43–56, 2019. DOI: 10.1108/lht-11-2017-0241.
Jorge Guerra, Wei Quan, Kai Li, Luis Ahumada, Flaura Winston, and Bimal Desai. Scosy: A biomedical collaboration recommendation system. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 3987–3990, 2018. DOI: 10.1109/EMBC.2018.8513268.
Yitong Hu, Zixuan Zhu, Yizhe Wang, Junxiang Wang, and Zehao Xing. A novel scholar embedding model for interdisciplinary collaboration. In Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things, CNIOT ’23, page 500–505, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400700705. DOI: 10.1145/3603781.3603869.
D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2010. ISBN 9781139492591. URL [link].
Tian Jin, Qiong Wu, Xuan Ou, and Jianjun Yu. Community detection and co-author recommendation in co-author networks. International Journal of Machine Learning and Cybernetics, 12:1–13, 02 2021. DOI: 10.1007/s13042-020-01190-8.
Xiaoyu Liu, Kun Wu, Biao Liu, and Rong Qian. Hnerec: Scientific collaborator recommendation model based on heterogeneous network embedding. Information Processing & Management, 60(2):103253, 2023. ISSN 0306-4573. DOI: 10.1016/j.ipm.2022.103253. URL [link].
Ilya Makarov, Olga Gerasimova, Pavel Sulimov, and Leonid E. Zhukov. Recommending co-authorship via network embeddings and feature engineering: The case of national research university higher school of economics. In Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, JCDL ’18, page 365–366, New York, NY, USA, 2018. Association for Computing Machinery. ISBN 9781450351782. DOI: 10.1145/3197026.3203911.
Ilya Makarov, Olga Gerasimova, Pavel Sulimov, and Leonid Zhukov. Dual network embedding for representing research interests in the link prediction problem on co-authorship networks. PeerJ Computer Science, 5:e172, 01 2019. DOI: 10.7717/peerj-cs.172.
S. Molaei, S. Babaei, M. Salehi, and M. Jalili. Information spread and topic diffusion in heterogeneous information networks. Scientific Reports, 8, 2018. DOI: 10.1038/s41598-018-27385-2.
Lara Souto Moreira, Gabriel Machado Lunardi, Matheus de Oliveira Ribeiro, Williamson Silva, and Fabio Paulo Basso. A study of algorithm-based detection of fake news in brazilian election: Is bert the best. IEEE Latin America Transactions, 21(8):897–903, 2023.
Hansong Nie, Xiangtai Chen, Xinbei Chu, Wei Wang, Zhenzhen Xu, and Feng Xia. Collaborator recommendation based on dynamic attribute network representation learning. In 2020 7th International Conference on Behavioural and Social Computing (BESC), 2020. DOI: 10.1109/BESC51023.2020.9348323.
Jason Portenoy, Marissa Radensky, Jevin D West, Eric Horvitz, Daniel S Weld, and Tom Hope. Bursting scientific filter bubbles: Boosting innovation via novel author discovery. CHI ’22, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450391573. DOI: 10.1145/3491102.3501905.
Tribikram Pradhan and Sukomal Pal. A multi-level fusion based decision support system for academic collaborator recommendation. Knowledge-Based Systems, 197:105784, 2020. ISSN 0950-7051. DOI: 10.1016/j.knosys.2020.105784. URL [link].
Vo Duc Quang, Hoang Huu Viet, Vu Hoang Long, and Tran Dinh Khang. An improved ad-aboost algorithm for highly imbalanced datasets in the co-authorship recommendation problem. IEEE Access, 11:89107–89123, 2023. DOI: 10.1109/ACCESS.2023.3306783.
Marcos W. Rodrigues, Wladmir C. Brandao, and Luis E. Zarate. Recommending scientific collaboration from researchgate. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pages 336–341, 2018. DOI: 10.1109/BRACIS.2018.00065.
M. Tarafdar and R. M. Davison. Research in information systems: intra-disciplinary and interdisciplinary approaches. Journal of the Association for Information Systems, 19:523–551, 2018. DOI: 10.17705/1jais.00500.
Wei Wang, Jiaying Liu, Tao Tang, Suppawong Tuarob, Feng Xia, Zhiguo Gong, and Irwin King. Attributed collaboration network embedding for academic relationship mining. ACM Trans. Web, 15(1), nov 2020. ISSN 1559-1131. DOI: 10.1145/3409736.
Mengjia Wu, Yi Zhang, Jie Lu, Hua Lin, and Mark Grosser. Recommending scientific collaborators: Bibliometric networks for medical research entities. In Developments of Artificial Intelligence Technologies in Computation and Robotics, pages 480–487, 2020. DOI: 10.1142/9789811223334, 0058. URL [link].
Xiaowen Xi, Jiaqi Wei, Ying Guo, and Weiyu Duan. Academic collaborations: a recommender framework spanning research interests and network topology. Scientometrics, 127(11):6787–6808, November 2022. ISSN 1588-2861. DOI: 10.1007/s11192-022-04555-8.
Yang Zhang, Chenwei Zhang, and Xiaozhong Liu. Dynamic scholarly collaborator recommendation via competitive multi-agent reinforcement learning. In Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys ’17, page 331–335, New York, NY, USA, 2017. Association for Computing Machinery. ISBN 9781450346528. DOI: 10.1145/3109859.3109914.
Xiaokang Zhou, Wei Liang, Kevin I-Kai Wang, Runhe Huang, and Qun Jin. Academic influence aware and multidimensional network analysis for research collaboration navigation based on scholarly big data. IEEE Transactions on Emerging Topics in Computing, 9(1): 246–257, 2021. DOI: 10.1109/TETC.2018.2860051.
Y. Zhu, J. Lin, S. He, B. Wang, Z. Guan, H. Liu, and D. Cai. Addressing the item cold-start problem by attribute-driven active learning. IEEE Transactions on Knowledge and Data Engineering, 32:631–644, 2020. DOI: 10.1109/tkde.2019.2891530.
Publicado
14/10/2024
Como Citar
MOREIRA, Lara S.; BASSO, Fábio P.; LUNARDI, Gabriel M.; SÁ, Guilherme B..
A Review and Analysis of Recommendation Systems in Collaboration Networks. In: RECOMENDAÇÃO SENSÍVEL AO CONTEXTO EM AMBIENTES INTELIGENTES (RESCAI) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 416-426.
DOI: https://doi.org/10.5753/sbbd_estendido.2024.243966.
