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Artificial Intelligence and Blockchain Technology Drive Leadership Decision-Making Research Group Recommendation Algorithm

School of Information and Electronics Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221000, P. R. China

Xuzhou Intelligent Machine Vision Engineering and Technology Center, Xuzhou 221116, P. R. China

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Yandong Liu

https://orcid.org/0009-0001-0054-7714

Xuzhou City Hospital of TCM, Xuzhou 221000, P. R. China

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Xin Wang

https://orcid.org/0009-0009-0005-5144

School of Information and Electronics Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221000, P. R. China

Xuzhou Intelligent Machine Vision Engineering and Technology Center, Xuzhou 221116, P. R. China

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, and

Yongsheng Xia

https://orcid.org/0000-0003-3136-9022

The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210000, P. R. China

E-mail Address: 920658920@qq.com

Corresponding author.

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https://doi.org/10.1142/S1469026824410013Cited by:0 (Source: Crossref)

Abstract

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members’ influence and leader impact. In this paper, a model has been proposed to compute members’ influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders’ judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms.

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