Reaching consensus in group recommendation systems

Abstract

Conventional group recommender systems fail to take into account the impact of group dynamics on group recommendations,
such as the process of reconciling individual preferences during collective decision-making. This scenario has been previously
examined in the context of group decision making, specifically in relation to consensus reaching procedures. In such processes,
experts engage in negotiations to determine their preferences and ultimately pick a mutually agreed upon option. The objective of the
consensus procedure is to prevent dissatisfaction among group members about the suggestion. Prior studies have tried to accomplish
this characteristic in group recommendation by using the minimal operator for the process of aggregating recommendations.
Nevertheless, the use of this operator ensures just a minimal degree of consensus on the proposal, but it does not provide a
satisfactory level of agreement among group members over the group recommendation. This paper focuses on analyzing consensus
reaching procedures in the context of group recommendation for group decision making. The goal of the study is to use consensus
reaching processes to provide group recommendations that satisfy all members of the group. Additionally, study aims to enhance
group recommender systems by ensuring an acceptable level of agreement among users regarding the group recommendation.
Therefore, group recommender systems are expanded by including consensus reaching mechanisms to facilitate group decision
making. In the context of group decision making, a collective resolution is reached by a group of persons, who may be specialists,
from a pool of options or potential solutions to the issue at hand. To do this, each specialist obtains their preferences about each
possibility. The conventional selection techniques for group decision-making difficulties fail to include the possibility of dissent
among experts over the chosen choice. This issue is alleviated by using consensus-building techniques, in which a substantial degree
of agreement is attained prior to picking the ultimate decision. To facilitate alignment of experts' tastes, they repeatedly modify them
to increase their proximity. Prior to making collective choices, it is sometimes necessary to establish a certain degree of consensus.
Thus, this paper presents a group recommendation architecture that utilizes automated consensus reaching models to provide
accepted suggestions. More specifically, we are considering the minimal cost consensus model and the automated consensus support
system model that relies on input. The minimal cost consensus model calculates the collective suggestion of a group by adjusting
individual preferences based on a cost function. This is achieved via the use of linear programming. The feedback-based automated
consensus support system model mimics the interaction between group members and a moderator. The moderator offers adjustments
to individual suggestions in order to bring them closer together and achieve a high degree of agreement before generating the group
recommendation. Both models are assessed and contrasted with baseline procedures in the testing