Social network large-scale group decision-making (SN-LSGDM) has become an important research topic in the field of decision science. However, the current methods have some limitations: the trust network ignores the influence of opinions, the weights of decision-makers are subjective, and the consensus adjustment efficiency is low while ignoring individual differences. Therefore, this paper proposes a personalized consensus-reaching method for large-scale group decision-making based on similarity-corrected trust network. Firstly, a trust network correction method based on opinion similarity is constructed to adaptively optimize the initial trust relationship. Secondly, the entropy weight method is applied to realize the objective determination of decision maker weights based on opinion similarity and trust degree, avoiding the subjective bias caused by traditional preset coefficients. Thirdly, a reference matrix is determined based on the trust relationship, and differentiated personalized adjustment coefficients are designed, thereby forming an efficient personalized consensus reaching mechanism. Finally, the practicality of the proposed model is verified through an illustrative example of live-streaming e-commerce decision-making. In addition, simulation experiments and comparative analyses are conducted to highlight the superiority of the proposed model.
References
[1]
Yu, L. and Lai, K.K. (2011) A Distance-Based Group Decision-Making Methodology for Multi-Person Multi-Criteria Emergency Decision Support. Decision Support Systems, 51, 307-315. https://doi.org/10.1016/j.dss.2010.11.024
[2]
Sun, B., Qi, C., Ma, W., Wang, T., Zhang, L. and Jiang, C. (2020) Variable Precision Diversified Attribute Multigranulation Fuzzy Rough Set-Based Multi-Attribute Group Decision Making Problems. Computers & Industrial Engineering, 142, Article ID: 106331. https://doi.org/10.1016/j.cie.2020.106331
[3]
Tang, M. and Liao, H. (2024) Group Efficiency and Individual Fairness Tradeoff in Making Wise Decisions. Omega, 124, Article ID: 103015. https://doi.org/10.1016/j.omega.2023.103015
[4]
Palomares, I., Martínez, L. and Herrera, F. (2014) A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making. IEEE Transactions on Fuzzy Systems, 22, 516-530. https://doi.org/10.1109/tfuzz.2013.2262769
[5]
Tang, M. and Liao, H. (2021) From Conventional Group Decision Making to Large-Scale Group Decision Making: What Are the Challenges and How to Meet Them in Big Data Era? A State-Of-The-Art Survey. Omega, 100, Article ID: 102141. https://doi.org/10.1016/j.omega.2019.102141
[6]
Garcia-Zamora, D., Labella, A., Ding, W., Rodriguez, R.M. and Martinez, L. (2022) Large-Scale Group Decision Making: A Systematic Review and a Critical Analysis. IEEE/CAA Journal of Automatica Sinica, 9, 949-966. https://doi.org/10.1109/jas.2022.105617
[7]
Chu, J., Wang, Y., Liu, X. and Liu, Y. (2020) Social Network Community Analysis Based Large-Scale Group Decision Making Approach with Incomplete Fuzzy Preference Relations. Information Fusion, 60, 98-120. https://doi.org/10.1016/j.inffus.2020.02.005
[8]
Liao, H., Li, X. and Tang, M. (2021) How to Process Local and Global Consensus? A Large-Scale Group Decision Making Model Based on Social Network Analysis with Probabilistic Linguistic Information. Information Sciences, 579, 368-387. https://doi.org/10.1016/j.ins.2021.08.014
[9]
Sun, X., Zhu, J., Wang, J., Pérez-Gálvez, I.J. and Cabrerizo, F.J. (2024) Consensus-reaching Process in Multi-Stage Large-Scale Group Decision-Making Based on Social Network Analysis: Exploring the Implication of Herding Behavior. Information Fusion, 104, Article ID: 102184. https://doi.org/10.1016/j.inffus.2023.102184
[10]
Liang, Y., Ju, Y., Zeng, X., Li, H., Dong, P. and Ju, T. (2025) A User-Generated Content-Based Social Network Large-Scale Group Decision-Making Approach in Healthcare Service: Case Study of General Practitioners Selection in UK. Expert Systems with Applications, 261, Article ID: 125542. https://doi.org/10.1016/j.eswa.2024.125542
[11]
Zhao, S., Lei, T., Liang, C., Na, J. and Liu, Y. (2022) A Consensus-Reaching Method for Large-Scale Group Decision-Making Based on Integrated Trust-Opinion Similarity Relationships. Computers & Industrial Engineering, 173, Article ID: 108667. https://doi.org/10.1016/j.cie.2022.108667
[12]
Meng, F., Li, H. and Li, J. (2025) An Interactive Iteration Consensus Based Social Network Large-Scale Group Decision Making Method and Its Application in Zero-Waste City Evaluation. Information Fusion, 115, Article ID: 102744. https://doi.org/10.1016/j.inffus.2024.102744
[13]
Huo, H., Sun, R., He, H. and Ren, Z. (2024) A Large-Scale Group Decision-Making Model Considering Expert Authority Degree and Relationship Evolution under Social Network. Group Decision and Negotiation, 33, 839-881. https://doi.org/10.1007/s10726-024-09892-y
[14]
Ren, R., Tang, M. and Liao, H. (2020) Managing Minority Opinions in Micro-Grid Planning by a Social Network Analysis-Based Large Scale Group Decision Making Method with Hesitant Fuzzy Linguistic Information. Knowledge-Based Systems, 189, Article ID: 105060. https://doi.org/10.1016/j.knosys.2019.105060
[15]
Zadeh, L.A. (1975) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—I. Information Sciences, 8, 199-249. https://doi.org/10.1016/0020-0255(75)90036-5
[16]
Martinez, L. and Herrera, F. (2000) A 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words. IEEE Transactions on Fuzzy Systems, 8, 746-752. https://doi.org/10.1109/91.890332
[17]
Rodriguez, R.M., Martinez, L. and Herrera, F. (2012) Hesitant Fuzzy Linguistic Term Sets for Decision Making. IEEE Transactions on Fuzzy Systems, 20, 109-119. https://doi.org/10.1109/tfuzz.2011.2170076
[18]
Pang, Q., Wang, H. and Xu, Z. (2016) Probabilistic Linguistic Term Sets in Multi-Attribute Group Decision Making. Information Sciences, 369, 128-143. https://doi.org/10.1016/j.ins.2016.06.021
[19]
Xiao, H., Wu, S. and Wang, L. (2022) A Novel Method to Estimate Incomplete PLTS Information Based on Knowledge-Match Degree with Reliability and Its Application in LSGDM Problem. Complex & Intelligent Systems, 8, 5011-5026. https://doi.org/10.1007/s40747-022-00723-8
[20]
Zou, W., Wan, S. and Dong, J. (2024) Trust Evolution Based Minimum Adjustment Consensus Framework with Dynamic Limited Compromise Behavior for Probabilistic Linguistic Large Scale Group Decision-making. Information Sciences, 652, Article ID: 119724. https://doi.org/10.1016/j.ins.2023.119724
[21]
Xiao, J., Wang, X., Gao, Y. and Xu, Z. (2025) Probability Completion and Consensus Reaching Based on Kernel Density Estimation for Incomplete Probabilistic Linguistic Multi-Attribute Group Decision Making. Information Sciences, 715, Article ID: 122207. https://doi.org/10.1016/j.ins.2025.122207
[22]
Liu, N., Zhang, X. and Wu, H. (2025) A Consensus-Reaching Model Considering Decision-Makers’ Willingness in Social Network-Based Large-Scale Group Decision-making. Information Fusion, 116, Article ID: 102797. https://doi.org/10.1016/j.inffus.2024.102797
[23]
Tu, Y., Wang, W., Ma, Z., Li, Z. and Lev, B. (2026) Multi-agent Reinforcement Learning-Based Consensus Building for Large-Scale Group Decision Making. Information Fusion, 126, Article ID: 103629. https://doi.org/10.1016/j.inffus.2025.103629
[24]
Liu, P., He, Z., Dong, X. and Wang, P. (2025) A Social Trust Network-Based Classification Consensus Decision-Making Method with Incomplete Information: Cross-Classification Adjustment Perspective. Computers & Industrial Engineering, 205, Article ID: 111190. https://doi.org/10.1016/j.cie.2025.111190
[25]
Chen, Y. and Wang, Y. (2025) Large-scale Group Consensus Decision Making in Social Networks Considering Adverse Selection in an Asymmetric Information Environment. Computers & Industrial Engineering, 205, Article ID: 111155. https://doi.org/10.1016/j.cie.2025.111155
[26]
Wang, Y., Cai, M. and Jian, X. (2023) Consensus Model of Social Network Group Decision-Making Based on Trust Relationship among Experts and Expert Reliability. Journal of Systems Engineering and Electronics, 34, 1576-1588. https://doi.org/10.23919/jsee.2023.000021
[27]
Xu, X., Hou, Y., He, J. and Zhang, Z. (2020) A Two-Stage Similarity Clustering-Based Large Group Decision-Making Method with Incomplete Probabilistic Linguistic Evaluation Information. Soft Computing, 24, 16869-16883. https://doi.org/10.1007/s00500-020-04981-x
[28]
Tian, X., Ma, W., Wu, L., Xie, M. and Kou, G. (2024) Large-Scale Consensus with Dynamic Trust and Optimal Reference in Social Network under Incomplete Probabilistic Linguistic Circumstance. Information Sciences, 661, Article ID: 120123. https://doi.org/10.1016/j.ins.2024.120123
[29]
Wang, Z., Liu, H. and Ma, R. (2025) A Probabilistic Linguistic Large-Group Emergency Decision-Making Method Based on the Louvain Algorithm and Group Pressure Model. Mathematics, 13, Article 670. https://doi.org/10.3390/math13040670
[30]
Sun, X. and Zhu, J. (2023) Large-Scale Group Classification Decision Making Method and Its Application with Trust-Interest Dual Factors in Social Network. Applied Soft Computing, 133, Article ID: 109890. https://doi.org/10.1016/j.asoc.2022.109890
[31]
Han, X., Zhan, J., Kou, G. and Herrera-Viedma, E. (2025) Enhancing Fairness and Efficiency in Tourism Accommodation Selection: A Probabilistic Linguistic Approach with Social Network Integration. Advanced Engineering Informatics, 68, Article ID: 103727. https://doi.org/10.1016/j.aei.2025.103727
[32]
Ahlim, W.S.A.W., Kamis, N.H., Ahmad, S.A.S. and Chiclana, F. (2021) Similarity–trust Network for Clustering‐based Consensus Group Decision‐making Model. International Journal of Intelligent Systems, 37, 2758-2773. https://doi.org/10.1002/int.22610
[33]
Yang, W. and Wang, Y. (2026) Adaptive Personalized Large-Scale Group Decision-Making Model Based on Psychobehavior in Comprehensive Trust Network. Expert Systems with Applications, 299, Article ID: 129863. https://doi.org/10.1016/j.eswa.2025.129863
[34]
Xing, Y., Wang, S., Dong, Y., Liu, Y. and Wu, J. (2024) The Trust Incentive Mechanism by Trust Propagation to Optimize Consensus in Social Network Group Decision Making. Expert Systems with Applications, 257, Article ID: 125111. https://doi.org/10.1016/j.eswa.2024.125111
[35]
Xu, Z.S. (2004) EOWA and EOWG Operators for Aggregating Linguistic Labels Based on Linguistic Preference Relations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 791-810. https://doi.org/10.1142/s0218488504003211
[36]
Wu, X. and Liao, H. (2019) A Consensus-Based Probabilistic Linguistic Gained and Lost Dominance Score Method. European Journal of Operational Research, 272, 1017-1027. https://doi.org/10.1016/j.ejor.2018.07.044
[37]
Blondel, V.D., Guillaume, J., Lambiotte, R. and Lefebvre, E. (2008) Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008. https://doi.org/10.1088/1742-5468/2008/10/p10008
[38]
Zhu, Y., Tian, D. and Yan, F. (2020) Effectiveness of Entropy Weight Method in Decision-Making. Mathematical Problems in Engineering, 2020, Article ID: 3564835. https://doi.org/10.1155/2020/3564835
