A novel approach to exploring maximum consensus graphs from users preference data in a new age environment
Many methods have been used to produce coherent aggregated results from individuals preference data, such as decision-making support systems, group recommendation systems, and so on. This study proposes a new framework where a graph model is used to represent user preferences and develops a new algorithm for detecting the maximum consensus and majority user group. The maximum consensus graph can be used to illustrate the preferences of the majority of the users. Similarly, discovering the segment of users who belong to the majority is useful information for the decision maker in order to produce consensus opinions and for market mangers to propose the most feasible strategies. In this study we initiate a new approach to the group ranking problem. Experiments using synthetic and real datasets show the models computational efficiency, scalability, and effectiveness.
data mining decision making group decision making maximum consensus sequence graph mining