Mining consensus preference graphs from users' ranking data

Yen Liang Chen, Li Chen Cheng, Po Hsiang Huang

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)1055-1064
Number of pages10
JournalDecision Support Systems
Volume54
Issue number2
DOIs
StatePublished - Jan 2013

Keywords

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

Fingerprint

Dive into the research topics of 'Mining consensus preference graphs from users' ranking data'. Together they form a unique fingerprint.

Cite this