Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 10, Pages 11081-11092Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3076420
Keywords
Social networking (online); Decision making; Numerical models; Mathematical model; Linguistics; Fuzzy sets; Data science; Consensus; group decision making (GDM); minimum cost; personalized feedback; social network; uninorm interval trust propagation
Categories
Funding
- National Natural Science Foundation of China (NSFC) [71971135, 71571166, 71910107002]
- Spanish State Research Agency [PID2019-103880RB-I00/AEI/10.13039/501100011033]
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The study establishes a two-fold personalized feedback mechanism in social network group decision-making, which achieves a balance between group consensus and individual personality by generating trusted recommendation advice and personalized adoption coefficients.
A two-fold personalized feedback mechanism is established for consensus reaching in social network group decision-making (SN-GDM). It consists of two stages: 1) generating the trusted recommendation advice for individuals and 2) producing a a personalized adoption coefficient for reducing unnecessary adjustment costs. A uninorm interval-valued trust propagation operator is developed to obtain an indirect trust relationship, which is used to generate personalized recommendation advice based on the principle of ``a recommendation being more acceptable the higher the level of trust it derives from.'' An optimization model is built to minimize the total adjustment cost of reaching consensus by determining the personalized feedback adoption coefficient based on individuals' consensus levels. Consequently, the proposed two-fold personalized feedback mechanism achieves a balance between group consensus and individual personality. An example to demonstrate how the proposed two-fold personalized feedback mechanism works is included, which is also used to show its rationality by comparing it with the traditional feedback mechanism in group decision making (GDM).
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