Group decision support with the analytic hierarchy process
Decision Support Systems
Aggregation of partial ordinal rankings: an interval goal programming approach
Computers and Operations Research
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking
Management Science
An agent model based on ideas of concordance and discordance for group ranking problems
Decision Support Systems
Methodologies and Algorithms for Group-Rankings Decision
Management Science
A novel collaborative filtering approach for recommending ranked items
Expert Systems with Applications: An International Journal
Journal of Artificial Intelligence Research
Emphasizing the rank positions in a distance-based aggregation procedure
Decision Support Systems
A framework for dynamic multiple-criteria decision making
Decision Support Systems
Dominance-based rough set approach for groups in multicriteria classification problems
Decision Support Systems
Mining consensus preference graphs from users' ranking data
Decision Support Systems
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
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The group ranking problem is used to construct coherent aggregate results from preference data provided by decision makers. Although there have been different input formats used to represent user preferences, they share a common weakness, that the input mode is static. In other words, users must provide all the preference data at one time. To overcome this weakness, we propose a framework which allows users to provide partial and/or incomplete preference data at multiple times. Since this is a complicated issue, we specifically focus on a particular aspect as a first attempt at this framework. Accordingly, we reexamine a variant of the group ranking problem, the maximum consensus mining problem, which will give the longest ranking lists of alternatives that agree with the majority and disagree only with the minority, under the dynamic input mode assumption. An algorithm is developed to determine the maximum consensus sequences from the users' partial ranking data. Finally, extensive experiments are carried out using synthetic data sets. The results indicate that the proposed method is computationally efficient, and can effectively identify consensus among all users.