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Mining maximum consensus sequences from group ranking data

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  • Chen, Yen-Liang
  • Cheng, Li-Chen

Abstract

In the last decade, the problem of getting a consensus group ranking from all users' ranking data has received increased attention due to its widespread applications. Previous research solved this problem by consolidating the opinions of all users, thereby obtaining an ordering list of all items that represent the achieved consensus. The weakness of this approach, however, is that it always produces a ranking list of all items, regardless of how many conflicts exist among users. This work rejects the forced agreement of all items. Instead, we define a new concept, maximum consensus sequences, which are the longest ranking lists of items that agree with the majority and disagree only with the minority. Based on this concept, algorithm MCS is developed to determine the maximum consensus sequences from users' ranking data, and also to identify conflict items that need further negotiation. Extensive experiments are carried out using synthetic data sets, and the results indicate that the proposed method is computationally efficient. Finally, we discuss how the identified consensus sequences and conflict items information can be used in practice.

Suggested Citation

  • Chen, Yen-Liang & Cheng, Li-Chen, 2009. "Mining maximum consensus sequences from group ranking data," European Journal of Operational Research, Elsevier, vol. 198(1), pages 241-251, October.
  • Handle: RePEc:eee:ejores:v:198:y:2009:i:1:p:241-251
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    References listed on IDEAS

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    Cited by:

    1. Nakagawa, Yoshinori & Nasu, Seigo & Saito, Taiki & Yamaguchi, Nobuyoshi, 2010. "Analytic hierarchy based policy design method (AHPo) for solving societal problems that require a multifaceted approach," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1545-1553, December.
    2. Li-Ching Ma, 2018. "Discovering Consensus Preferences Visually Based on Gower Plots," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 741-761, May.
    3. Ma, Li-Ching, 2016. "A new group ranking approach for ordinal preferences based on group maximum consensus sequences," European Journal of Operational Research, Elsevier, vol. 251(1), pages 171-181.
    4. Cheng, Li-Chen & Chen, Yen-Liang & Chiang, Yu-Chia, 2016. "Identifying conflict patterns to reach a consensus – A novel group decision approach," European Journal of Operational Research, Elsevier, vol. 254(2), pages 622-631.
    5. Wei-Feng Tung & Yu-Jei Lan, 2017. "Analyzing social choice and group ranking of online games for product mix innovation," Information Systems Frontiers, Springer, vol. 19(6), pages 1301-1309, December.
    6. Huang, Tony Cheng-Kui, 2013. "A novel group ranking model for revealing sequence and quantity knowledge," European Journal of Operational Research, Elsevier, vol. 231(3), pages 654-666.

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