Two-Fold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation

Area of Science:

• Decision Sciences
• Social Network Analysis
• Artificial Intelligence

Background:

• Group decision-making in social networks often struggles with consensus.
• Individual preferences can conflict with group objectives, leading to inefficiencies.

Purpose of the Study:

• To develop a two-fold personalized feedback mechanism for social network group decision-making (SN-GDM).
• To balance group consensus with individual personality in decision processes.

Main Methods:

• A uninorm interval-valued trust propagation operator was developed to establish indirect trust relationships.
• Personalized recommendation advice was generated based on trust levels.
• An optimization model determined personalized feedback adoption coefficients to minimize adjustment costs.

Main Results:

• The proposed mechanism effectively generates trusted recommendation advice.
• It successfully determines personalized adoption coefficients to reduce adjustment costs.
• The system achieves a balance between group consensus and individual personality.

Conclusions:

• The novel two-fold personalized feedback mechanism enhances consensus reaching in SN-GDM.
• It offers a rational and efficient approach compared to traditional methods.
• This mechanism supports more effective group decision-making in social networks.