Mining maximum consensus sequences from group ranking data

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > journal & Dissertation > Item 987654321/29666

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29666

Title:	Mining maximum consensus sequences from group ranking data
Authors:	Chen,YL;Cheng,LC
Contributors:	資訊管理研究所
Keywords:	PARTIAL ORDINAL RANKINGS;PREFERENCE STRUCTURES;DECISION;AGGREGATION;PROPOSALS;DISTANCES;REVIEWERS;SET
Date:	2009
Issue Date:	2010-06-29 20:37:44 (UTC+8)
Publisher:	中央大學
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. (C) 2008 Elsevier B.V. All rights reserved.
Relation:	EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

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