Mining consensus preference graphs from users' ranking data

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/106864

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106864

Title:	Mining consensus preference graphs from users' ranking data
Authors:	陳彥良;Chen, Yen-Liang;Cheng, Li-Chen;Huang, Po-Hsiang
Contributors:	管理學院資訊管理學系
Keywords:	Algorithms;Applied sciences;Artificial intelligence;Coherence;Computational efficiency;Computer science;control theory;systems;Computer systems and distributed systems. User interface;Connectionism. Neural networks;Construction;Data mining;Data processing. List processing. Character string processing;Decision making;Decision making models;Decision support systems;Decision theory. Utility theory;Exact sciences and technology;Graph algorithms;Graph representations;Graphs;Group decision making;Maximum consensus sequence;Memory organisation. Data processing;Operational research and scientific management;Operational research. Management science;Preference graph;Preferences;Ranking;Ratings & rankings;Software;Studies
Date:	2013-01-01
Issue Date:	2026-04-23 13:47:01 (UTC+8)
Publisher:	Elsevier;Amsterdam: Elsevier B.V
Abstract:	摘要： 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. ► This study proposes a new output type of group ranking problem. ► The new output is a consensus preference graph of items. ► The output graph can be understood intuitively and the relationships between items can be easily seen. ► The experimental results show that the proposed method is efficient and effective. 出版者： Amsterdam: Elsevier B.V 出版日期： 2013-01 出處： Decision Support Systems, 2013-01, Vol.54 (2), p.1055-1064 資源來源： Elsevier ScienceDirect Journals Complete - Autoholdings 版權： 2012 Elsevier B.V. 版權： 2014 INIST-CNRS 版權： Copyright Elsevier Sequoia S.A. Jan 2013 識別號： ISSN: 0167-9236 識別號： EISSN: 1873-5797 識別號： DOI: 10.1016/j.dss.2012.10.031 識別號： CODEN: DSSYDK
Appears in Collections:	[Department of Information Management] journal & Dissertation

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