Mining consensus preference graphs from users' ranking data

Yen Liang Chen; Li Chen Cheng; Po Hsiang Huang

doi:10.1016/j.dss.2012.10.031

Mining consensus preference graphs from users' ranking data

Yen Liang Chen, Li Chen Cheng, Po Hsiang Huang

Department of Information Management

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

The group ranking problem consists of constructing coherent aggregated results from preference data provided by decision makers. Traditionally, the output of a group ranking problem can be classified into ranking lists and maximum consensus sequences. In this study, we propose a consensus preference graph approach to represent the coherent aggregated results of users' preferences. The advantages of our approach are that (1) the graph is built based on users' consensuses, (2) the graph can be understood intuitively, and (3) the relationships between items can be easily seen. An algorithm is developed to construct the consensus preference graph from users' total ranking data. Finally, extensive experiments are carried out using synthetic and real data sets. The experimental results indicate that the proposed method is computationally efficient, and can effectively identify consensus graphs.

Original language	English
Pages (from-to)	1055-1064
Number of pages	10
Journal	Decision Support Systems
Volume	54
Issue number	2
DOIs	https://doi.org/10.1016/j.dss.2012.10.031
State	Published - Jan 2013

Keywords

Data mining
Decision making
Group decision making
Maximum consensus sequence
Preference graph

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10.1016/j.dss.2012.10.031

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title = "Mining consensus preference graphs from users' ranking data",

abstract = "The group ranking problem consists of constructing coherent aggregated results from preference data provided by decision makers. Traditionally, the output of a group ranking problem can be classified into ranking lists and maximum consensus sequences. In this study, we propose a consensus preference graph approach to represent the coherent aggregated results of users' preferences. The advantages of our approach are that (1) the graph is built based on users' consensuses, (2) the graph can be understood intuitively, and (3) the relationships between items can be easily seen. An algorithm is developed to construct the consensus preference graph from users' total ranking data. Finally, extensive experiments are carried out using synthetic and real data sets. The experimental results indicate that the proposed method is computationally efficient, and can effectively identify consensus graphs.",

keywords = "Data mining, Decision making, Group decision making, Maximum consensus sequence, Preference graph",

author = "Chen, {Yen Liang} and Cheng, {Li Chen} and Huang, {Po Hsiang}",

note = "Funding Information: It is our pleasure to acknowledge the anonymous reviewers for their valuable suggestions and the careful reading of our manuscript. The authors would like to express our gratitude to these reviewers for their suggestions that helped to substantially improve our paper. This study was supported by the National Science Council of Taiwan under grant NSC 97-2410-H-031-056 and 101-2410-H-008-008-MY3 . ",

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AU - Cheng, Li Chen

AU - Huang, Po Hsiang

N1 - Funding Information: It is our pleasure to acknowledge the anonymous reviewers for their valuable suggestions and the careful reading of our manuscript. The authors would like to express our gratitude to these reviewers for their suggestions that helped to substantially improve our paper. This study was supported by the National Science Council of Taiwan under grant NSC 97-2410-H-031-056 and 101-2410-H-008-008-MY3 .

PY - 2013/1

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N2 - The group ranking problem consists of constructing coherent aggregated results from preference data provided by decision makers. Traditionally, the output of a group ranking problem can be classified into ranking lists and maximum consensus sequences. In this study, we propose a consensus preference graph approach to represent the coherent aggregated results of users' preferences. The advantages of our approach are that (1) the graph is built based on users' consensuses, (2) the graph can be understood intuitively, and (3) the relationships between items can be easily seen. An algorithm is developed to construct the consensus preference graph from users' total ranking data. Finally, extensive experiments are carried out using synthetic and real data sets. The experimental results indicate that the proposed method is computationally efficient, and can effectively identify consensus graphs.

AB - The group ranking problem consists of constructing coherent aggregated results from preference data provided by decision makers. Traditionally, the output of a group ranking problem can be classified into ranking lists and maximum consensus sequences. In this study, we propose a consensus preference graph approach to represent the coherent aggregated results of users' preferences. The advantages of our approach are that (1) the graph is built based on users' consensuses, (2) the graph can be understood intuitively, and (3) the relationships between items can be easily seen. An algorithm is developed to construct the consensus preference graph from users' total ranking data. Finally, extensive experiments are carried out using synthetic and real data sets. The experimental results indicate that the proposed method is computationally efficient, and can effectively identify consensus graphs.

KW - Data mining

KW - Decision making

KW - Group decision making

KW - Maximum consensus sequence

KW - Preference graph

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ER -

Mining consensus preference graphs from users' ranking data

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Keywords

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