Mining maximum consensus sequences from group ranking data

• Main Authors: Li-chen Cheng, 鄭麗珍
• Other Authors: Yen-Liang Chen
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/58303646266345835813

博士 === 國立中央大學 === 資訊管理研究所 === 96 === In the last decade, the problem of getting a consensus group ranking from 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, we use two kinds of input data, individual’s total ranking and individual’s partial rankings, to develop algorithms to discover maximum consensus sequences and also to identify conflict items that need further negotiation. Besides, we propose another algorithm to achieve personalized rankling list which can be used in recommender system. Extensive experiments are carried out using synthetic data sets, and the results indicate that the proposed methods are computationally efficient.