目前許多領域都會運用群體排序資料做不同的應用，例如：群體決策、機器學習、網路搜尋技術…等。這些應用都希望能在已知的群體排序資料中找出一個最有共識的結果，因此，群體排序問題儼然成為一項重要的議題。過去的研究多半試圖利用不同的演算法以產生一個單一的排序結果，並以此做為所謂的群體共識。然而，這類研究縱使遇到群體之間的資料充滿衝突或是共識性很低時，仍然會產生一個排序結果，這樣的方式其實是很不適當的，會誤導決策者一個錯誤的方向。因此，本文提出『最大共識』的觀念，希望能透過我們所提出的演算法，不但可以找出群體間『最大共識』的資料之外，還能指出群體資料衝突之處，這樣才真的可以幫助決策者做進一步的協調與溝通以尋求最後的共識。因此，本文針對使用者提供資訊的完成程度，區分成兩種不同類型的來源資料，一是完整排序資料，另一則是允許使用者提供多個部分排序資料，以此分別提出不同的演算法。另一方面也提出方法找出個人化的排序資料以運用在推薦系統上。這些方法經由一連串的實驗過程(包含人工資料與真實資料)，證明本文所提出的方法不論在效率與效果方面都有不錯的表現。 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.
