從序列資料中找尋偏好圖的方法 - 應用於群體排名問題

在過去的十年中，如何從所有使用者的排名資料，取得其中一致的排名這個問題，由於其廣泛的應用，導致此問題日益受到重視。而此群體排名問題的應用主要是為了提供一個資料的綜合結果給決策者。傳統上。這問題的輸出可以分為兩種型態：排名序列與共識序列，而不幸的是，這兩種傳統的輸出都有各自的缺點。對於排名序列，大多數以前的辦法是最小化多個輸入資料之間的分歧，而取得代表大部分使用者共識的排名序列，但為了獲得一個完整的排名，他們會忽略使用者的資料可能會不一致或沒有共識的事實，而強迫進行排序。而對於共識序列來說，為了獲得最大的共識序列，可能導致許多共識序列，需要進行檢查，從而導致信息超載，乏味，不容易理解。並且從眾多的共識序列終，決策者難以掌握整個項目之間的關係。　　為了克服這些缺點，我們提出了一個框架，它產生的項目偏好圖代表使用者喜好資料的綜合結果。並開發一個演算法來決定使用者排序資料中的偏好圖形式。最後，進行了廣泛的實驗，使用合成和真實資料集。實驗結果表明，該方法計算效率高，能有效地識別所有使用者之間的共識。

關鍵字

偏好圖；決策制定；資料挖掘

並列摘要

In the last decade, the problem of getting a consensus group ranking from all users’ ranking data has received increasing attention due to its widespread applications. The group ranking problem is to construct coherent aggregated results from preference data provided by decision makers. Traditionally, the output of the group ranking problem can be classified into two types: ranking ordered lists and consensus lists. Unfortunately, these two traditional outputs suffer from their own weaknesses. For ranking ordered lists, most previous approaches pay close attention to minimize the total disagreement between multiple input rankings, to ultimately obtain an overall ranking list which represents the achieved consensus. They neglect the fact that user opinions may be discordant and have no consensus, in which we are still enforced to get a complete ranking result. For maximum consensus sequences, there may have many resulting consensus sequences which need to be checked, and thus lead to information overloading, tedious, and not easy to understand. As a result, users are difficult to grasp the whole relationship among items. To overcome these weaknesses, we propose a framework which generates a preference graph of items to represent the coherent aggregated results of users’ preferences. And an algorithm is developed to determine the preference graph from the 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 among all users.

並列關鍵字

preference graph ； decision making ； data mining

參考文獻

[1] Cook, W. D., Golany, B., Kress, M., Penn, M., and Raviv, T., 2005, “Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking,” Management Science, Vol.51, No.4, pp.655-661.

[2] Cook, W. D., Golany, B., Penn, M., and Raviv, T., 2007, “Creating a Consensus Ranking of Proposals from Reviewer''s Partial Ordinal Rankings,” Computers & OR, Vol.34, No.4, pp.954-965.

[3] Fagin, R., Kumar, R., and Sivakumar, D., 2003, “Efficient similarity search and classification via rank aggregation,” Proceedings of the ACM SIGMOD International Conference on Management of Data, San Diego, California, pp.301-312.

[7] Golden, B., 1989, The analytic hierarchy process: applications and studies, Springer, New York.

[9] Kendall, M., 1955, Rank correlation methods, Third Hafner, New York.

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從序列資料中找尋偏好圖的方法 - 應用於群體排名問題

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