A novel approach to exploring maximum consensus graphs from users’ preference data in a new age environment
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Many methods have been used to produce coherent aggregated results from individual’s 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 model’s computational efficiency, scalability, and effectiveness.
KeywordsData mining Decision making Group decision making Maximum consensus sequence Graph mining
The authors are very grateful to the anonymous referees for their helpful comments and valuable suggestions for improving the earlier version of the paper. This research was supported by the Ministry of Science and Technology, Taiwan, R.O.C. under the Grant NSC 100-2410-H-031-010-MY2 and NSC 102-2410-H-031-058-MY3. We appreciate Tsai Hsieh Che to assistant program of this study.
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