Twofold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation

Date

2021-04

Advisors

Journal Title

Journal ISSN

ISSN

2168-2267

Volume Title

Publisher

IEEEXplore

Type

Article

Peer reviewed

Yes

Abstract

A twofold 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 personalized adoption coefficient for reducing unnecessary adjustment costs. This is achieved by means of a uninorm interval-valued trust propagation operator to obtain indirect trust. The trust relationship 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 minimise the total adjustment cost of reaching consensus by determining personalized feedback adoption coefficient based on individuals’ consensus levels. Consequently, the proposed twofold personalized feedback mechanism achieves a balance between group consensus and individual personality. An example to demonstrate how the proposed twofold personalized feedback mechanism works is included, which is also used to show its rationality by comparison with the traditional feedback mechanism in GDM.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Group decision making, Consensus, Social network, Uninorm interval trust propagation, Personalized feedback, Minimum cost

Citation

Wu, J., Wang, S., Chiclana, F., Herrera-Viedma, E. (2021) Twofold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation. IEEE Transactions on Cybernetics.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)