More Web Proxy on the site http://driver.im/

research-article

Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process

Authors:

Shui YuAuthors Info & Claims

ACM Transactions on Internet Technology, Volume 23, Issue 2

Article No.: 26, Pages 1 - 27

https://doi.org/10.1145/3533432

Published: 19 May 2023 Publication History

Abstract

When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.

References

[1]

Kenneth J. Arrow. 2012. Individual Values and Social Choice (3rd ed.). Yale University Press, New Haven, CT.

[2]

Dorit S. Hochbaum and Asaf Levin. 2006. Methodologies and algorithms for group-rankings decision. Manage. Sci. 52, 9 (Sept. 2006), 1394–1408.

Digital Library

[3]

Zhen Zhang, Wenyu Yu, Luis Martínez, and Yuan Gao. 2020. Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute large-scale group decision making: A linguistic distribution-based approach. IEEE Trans. Fuzzy Syst. 28, 11 (Nov. 2020), 2875–2889.

[4]

Xiangrui Chao, Gang Kou, Yi Peng, and Enrique Herrera-Viedma. 2021. Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. Eur. J. Oper. Res. 288, 1 (Jan. 2021), 271–293.

[5]

Francisco Herrera, Sergio Alonso, Francisco Chiclana, and Enrique Herrera-Viedma. 2009. Computing with words in decision making: Foundations, trends and prospects. Fuzzy Optim. Decis. Making 8, 4 (Dec. 2009), 337–364.

Digital Library

[6]

Enrique Herrera-Viedma, Iván Palomares, Cong-Cong Li, Francisco Javier Cabrerizo, Yucheng Dong, Francisco Chiclana, and Francisco Herrera. 2021. Revisiting fuzzy and linguistic decision making: Scenarios and challenges for making wiser decisions in a better way. IEEE Trans. Syst. Man Cybern. Syst. 51, 1 (Jan. 2021), 191–208.

[7]

Lotfi A. Zadeh. 1996. Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 2 (May 1996), 103–111.

Digital Library

[8]

Francisco Javier Cabrerizo, Enrique Herrera-Viedma, and Witold Pedrycz. 2013. A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. Eur. J. Oper. Res. 230, 3 (Nov. 2013), 624–633.

[9]

Yuzhu Wu, Zhen Zhang, Gang Kou, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li, Yucheng Dong, and Francisco Herrera. 2021. Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Inf. Fusion 65 (Jan. 2021), 165–178.

[10]

Francisco Herrera and Luis Martínez. 2000. A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8, 6 (Dec. 2000), 746–752.

Digital Library

[11]

Jerry Mendel and Dongrui Wu. 2010. Perceptual Computing: Aiding People in Making Subjective Judgments. John Wiley & Sons, Hoboken, NJ.

[12]

Cong-Cong Li, Yucheng Dong, Francisco Herrera, Enrique Herrera-Viedma, and Luis Martínez. 2017. Personalized individual semantics in computing with words for supporting linguistic group decision making: An application on consensus reaching. Inf. Fusion 33 (Jan. 2017), 29–40.

Digital Library

[13]

Cong-Cong Li, Yucheng Dong, Witold Pedrycz, and Francisco Herrera. 2022. Integrating continual personalized individual semantics learning in consensus reaching in linguistic group decision making. IEEE Trans. Syst. Man Cybern. Syst. 52, 3 (March 2022), 1525–1536.

[14]

Jing Xiao, Xiuli Wang, and Hengjie Zhang. 2020. Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making. Inf. Fusion 53 (Jan. 2020), 20–34.

Digital Library

[15]

Haiming Liang, Cong-Cong Li, Yucheng Dong, and Francisco Herrera. 2021. Linguistic opinions dynamics based on personalized individual semantics. IEEE Trans. Fuzzy Syst. 29, 9 (Sept. 2021), 2453–2466.

Digital Library

[16]

Xiangrui Chao, Gang Kou, Yi Peng, Enrique Herrera-Viedma, and Francisco Herrera. 2021. An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement. Inf. Sci. 575 (Oct. 2021), 499–527.

Digital Library

[17]

Janusz Kacprzyk and Mario Fedrizzi. 1988. A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences. Eur. J. Oper. Res. 34, 3 (March 1988), 316–325.

[18]

Yucheng Dong, Sihai Zhao, Hengjie Zhang, Francisco Chiclana, and Enrique Herrera-Viedma. 2018. A self-management mechanism for noncooperative behaviors in large-scale group consensus reaching processes. IEEE Trans. Fuzzy Syst. 26, 6 (Dec. 2018), 3276–3288.

Digital Library

[19]

You Sun, Rui Xue, Rui Zhang, Qianqian Su, and Sheng Gao. 2021. RTChain: A reputation system with transaction and consensus incentives for e-commerce blockchain. ACM Trans. Internet Technol. 21, 1 (Feb. 2021), 1–24.

Digital Library

[20]

Iván Palomares, Francisco J. Estrella, Luis Martínez, and Francisco Herrera. 2014. Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Inf. Fusion 20 (Nov. 2014), 252–271.

[21]

Hengjie Zhang, Sihai Zhao, Gang Kou, Cong-Cong Li, Yucheng Dong, and Francisco Herrera. 2020. An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Inf. Fusion 60 (Aug. 2020), 65–79.

[22]

Jian Wu, Lifang Dai, Francisco Chiclana, Hamido Fujita, and Enrique Herrera-Viedma. 2018. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Inf. Fusion 41 (May 2018), 232–242.

Digital Library

[23]

Fuying Jing and Xiangrui Chao. 2021. Fairness concern: An equilibrium mechanism for consensus-reaching game in group decision-making. Inf. Fusion 72 (Aug. 2021), 147–160.

[24]

David Camacho, Ángel Panizo-LLedot, Gema Bello-Orgaz, Antonio Gonzalez-Pardo, and Erik Cambria. 2020. The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Inf. Fusion 63 (Nov. 2020), 88–120.

[25]

Jinbaek Kim. 2008. A model and case for supporting participatory public decision making in e-democracy. Group Decis. Negot. 17, 3 (May 2008), 179–193.

[26]

Iván Palomares, Luis Martínez, and Francisco Herrera. 2014. A consensus model to detect and manage noncooperative behaviors in large-scale group decision making. IEEE Trans. Fuzzy Syst. 22, 3 (June 2014), 516–530.

Digital Library

[27]

Paulene Govender and Venkataraman Sivakumar. 2020. Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmos. Pollut. Res. 11, 1 (Jan. 2020), 40–56.

[28]

James C. Bezdek. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, NY.

[29]

Ru-Xi Ding, Iván Palomares, Xueqing Wang, Guo-Rui Yang, Bingsheng Liu, Yucheng Dong, Enrique Herrera-Viedma, and Francisco Herrera. 2020. Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective. Inf. Fusion 59 (July 2020), 84–102.

[30]

Yejun Xu, Xiaowei Wen, and Wancheng Zhang. 2018. A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Comput. Ind. Eng. 116 (Feb. 2018), 113–129.

[31]

Cong-Cong Li, Yucheng Dong, and Francisco Herrera. 2019. A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups. IEEE Trans. Fuzzy Syst. 27, 2 (Feb. 2019), 221–233.

[32]

Cong-Cong Li, Rosa M. Rodríguez, Luis Martínez, Yucheng Dong, and Francisco Herrera. 2018. Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions. Knowl. Based Syst. 145 (April 2018), 156–165.

Digital Library

[33]

Chunli Huang, Wenjun Jiang, Jie Wu, and Guojun Wang. 2020. Personalized review recommendation based on users’ aspect sentiment. ACM Trans. Internet Technol. 20, 4 (Nov. 2020), 1–26.

Digital Library

[34]

Yucheng Dong, Yinfeng Xu, and Shui Yu. 2009. Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Trans. Fuzzy Syst. 17, 6 (Dec. 2009), 1366–1378.

Digital Library

[35]

James C. Bezdek, Bonnie Spillman, and Richard Spillman. 1978. A fuzzy relation space for group decision theory. Fuzzy Set. Syst. 1, 4 (Oct. 1978), 255–268.

[36]

Enrique Herrera-Viedma, Francisco Herrera, Francisco Chiclana, and Maria Luque-Rodríguez. 2004. Some issues on consistency of fuzzy preference relations. Eur. J. Oper. Res. 154, 1 (April 2004), 98–109.

[37]

Sergio Alonso, Francisco Chiclana, Francisco Herrera, Enrique Herrera-Viedma, Jesus Alcalá-Fdez, and Carlos Porcel. 2008. A consistency-based procedure to estimate missing pairwise preference values. Int. J. Intell. Syst. 23, 2 (Feb. 2008), 155–175.

[38]

Joe H. Ward Jr. 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 301 (1963), 236–244.

[39]

James MacQueen. 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability 5.1 (Jan 1967), 281–297.

[40]

George Dantzig. 1963. Linear Programming and Extensions. Princeton University Press, Princeton, NJ.

[41]

Timothée Lesort, Vincenzo Lomonacoc, Andrei Stoianb, Davide Maltoni, David Filliat, and Natalia Díaz-Rodríguez. 2020. Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58 (June 2020), 52–68.

Digital Library

[42]

Anne H. Ngu, Mario Gutierrez, Vangelis Metsis, Surya Nepal, and Quan Z. Sheng. 2017. IoT middleware: A survey on issues and enabling technologies. IEEE Internet Things J. 4, 1 (Feb. 2017), 1–20.

[43]

Lina Yao, Quan Z. Sheng, Anne H. H. Ngu, and Xue Li. 2016. Things of interest recommendation by leveraging heterogeneous relations in the Internet of Things. ACM Trans. Internet Technol. 16, 2 (April 2016), 1–25.

Digital Library

[44]

Maggi Bansal, Inderveer Chana, and Siobhán Clarke. 2021. A survey on IoT big data: Current status, 13 V's challenges, and future directions. ACM Comput. Surv. 53, 6 (Feb. 2021), 1–59.

Digital Library

[45]

Xia Liu, Yejun Xu, and Francisco Herrera. 2019. Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: Detecting and managing overconfidence behaviors. Inf. Fusion 52 (Dec. 2019), 245–256.

Digital Library

[46]

Rosa M. Rodríguez, Luis Martínez, and Francisco Herrera. 2012. Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20, 1 (Feb. 2012), 109–119.

Digital Library

[47]

Guiqing Zhang, Yucheng Dong, and Yinfeng Xu. 2014. Consistency and consensus measures for linguistic preference relations based on distribution assessments. Inf. Fusion 17 (May 2014) 46–55.

Digital Library

Cited By

Li ZZhang Z(2025)Modeling Personalized Individual Semantics in Multicriteria Decision Making With Incomplete Linguistic Preference Relations: A Preference Disaggregation PerspectiveIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.347269955:1(390-403)Online publication date: Jan-2025
https://doi.org/10.1109/TSMC.2024.3472699
Wang YGao JLiu H(2025)A large-scale group decision making method with text mining and probabilistic linguistic complementation for energy transition path assessmentRenewable Energy10.1016/j.renene.2024.122169239(122169)Online publication date: Feb-2025
https://doi.org/10.1016/j.renene.2024.122169
Zhang STian HHu LDeveci MLiu X(2024)Managing flexible linguistic expressions with subjective preferences and objective information in group decision-making: A perspective based on personalized individual semanticsInformation Fusion10.1016/j.inffus.2024.102633(102633)Online publication date: Aug-2024
https://doi.org/10.1016/j.inffus.2024.102633
Show More Cited By

Index Terms

Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Lexical semantics

Recommendations

Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions
Abstract
In decision making problems, decision makers may prefer to use more flexible linguistic expressions instead of using only one linguistic term to express their preferences. The recent proposals of hesitant fuzzy linguistic terms sets (...
Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching

To propose a personalized individual semantics model (PIS).To propose personalized 2-tuple linguistic comparison and aggregation.To discuss the application of PIS to support linguistic consensus reaching. In group decision making (GDM) dealing with ...
Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making
Highlights
- Devise a framework to manage PIS and consensus in linguistic distribution LSGDM.
Abstract
A linguistic distribution assessment is an effective approach to represent uncertain preferences in large-scale group decision making (LSGDM). The same word often signifies different things for different decision makers in linguistic ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 23, Issue 2

May 2023

276 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3597634

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2023

Online AM: 06 May 2022

Accepted: 24 April 2022

Revised: 24 November 2021

Received: 30 April 2021

Published in TOIT Volume 23, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Sichuan University
Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
432
Total Downloads

Downloads (Last 12 months)159
Downloads (Last 6 weeks)19

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li ZZhang Z(2025)Modeling Personalized Individual Semantics in Multicriteria Decision Making With Incomplete Linguistic Preference Relations: A Preference Disaggregation PerspectiveIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.347269955:1(390-403)Online publication date: Jan-2025
https://doi.org/10.1109/TSMC.2024.3472699
Wang YGao JLiu H(2025)A large-scale group decision making method with text mining and probabilistic linguistic complementation for energy transition path assessmentRenewable Energy10.1016/j.renene.2024.122169239(122169)Online publication date: Feb-2025
https://doi.org/10.1016/j.renene.2024.122169
Zhang STian HHu LDeveci MLiu X(2024)Managing flexible linguistic expressions with subjective preferences and objective information in group decision-making: A perspective based on personalized individual semanticsInformation Fusion10.1016/j.inffus.2024.102633(102633)Online publication date: Aug-2024
https://doi.org/10.1016/j.inffus.2024.102633
Zhou XLi SWei C(2024)Consensus reaching process for group decision-making based on trust network and ordinal consensus measureInformation Fusion10.1016/j.inffus.2023.101969101(101969)Online publication date: Jan-2024
https://doi.org/10.1016/j.inffus.2023.101969
Yu JXu YYu YWu S(2024)A novel failure mode and effect analysis model using personalized linguistic evaluations and the rule-based Bayesian networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107345127(107345)Online publication date: Jan-2024
https://doi.org/10.1016/j.engappai.2023.107345
Xiao JWang X(2024)An optimization method for handling incomplete and conflicting opinions in quality function deployment based on consistency and consensus reaching processComputers & Industrial Engineering10.1016/j.cie.2023.109779187(109779)Online publication date: Jan-2024
https://doi.org/10.1016/j.cie.2023.109779
Wan QXu XHan J(2024)A dimensionality reduction method for large-scale group decision-making using TF-IDF feature similarity and information loss entropyApplied Soft Computing10.1016/j.asoc.2023.111039150:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111039
Li YKou GPeng YMorente-Molinera J(2024)Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papersApplied Intelligence10.1007/s10489-024-05765-854:21(10962-10981)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05765-8
Jing FXu MChao XHerrera-viedma E(2024)Mining emotion soft factors in linguistic preference time sequences based on personalized individual semantics in group decision-makingApplied Intelligence10.1007/s10489-024-05697-354:21(11120-11143)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05697-3
Zhang YDai YLiu B(2023)Identifying Qualified Public Safety Education Venues Using the Dempster–Shafer Theory-Based PROMETHEE Method under Linguistic EnvironmentsMathematics10.3390/math1104101111:4(1011)Online publication date: 16-Feb-2023
https://doi.org/10.3390/math11041011
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents