透過群體決策的過程發掘封閉式的時間性共識序列樣式

Discovery Of Closed Consensus Temporal Patterns In Group Decision Making

張正麒(Chen-Chi Chang)

指導教授 ： 黃正魁

國立中正大學/管理學院/企業管理學系/碩士(2011年)

摘要

將個人的喜好與偏好聚集成共識 (Consensus) 排序是一種決策支援上的問題，而這種方式已被廣泛應用於各種領域中，像是決策支援系統、評選系統、推薦系統等方面。特別是將推薦系統應用於商業的用途上，當消費者尋求有關於購買商品或服務上的建議時，可藉由系統所匯集的大量資訊有效地整理出一套大眾化的推薦訊息。因此，我們必須收集更多顧客的喜好，整理出大部份消費者的排序來完成共識序列。
舉個偏好排序的例子，“C＞A≧D≧B”這組偏好序列代表者消費者喜好上的優劣排序，即喜愛C的程度大於A；而A在某種程度上其喜愛程度是大於D,但又不完全的優於D（某種程度上可能是一致的），D與B的關係亦是如此。序列中符號＞與≧為比較的基準，A、B、C與D則為比較的項目。結合兩種元素就能表示出一組偏好序列的排序關係。然而，就現有的文獻中，尚未有研究去發展推薦系統中項目之間的時間性關連 (Temporal relationship between items)，例如A事件發生的時間點可能落在B事件的持續時間區段中，或是C事件發生在D事件之前。這類“事件發生先後”的推薦可應用於訂購讀物、大學課程規劃或是患者服藥的順序上。
在這項研究中，我們提出一個新的推薦模式去找出封閉式（Closed）的共識時間樣式；而「封閉」意指所得到的樣式是沒有被其他的共識序列所包含。此實驗亦藉由真實的樣本來資料來表現出些模型的有效性。藉由這樣的資訊來達到提早準備商品的服務或對應的商業政策。

關鍵字

封閉式樣式 ； 群體決策 ； 資料探勘 ； 共識時間性樣式

並列摘要

The aggregation of individuals’ preferences into a consensus ranking is a decision support problem which has been widely used in various applications, such as decision support systems, voting systems, and recommendation systems. Especially when applying recommendation systems in business, customers ask for more suggestions about purchasing products or services because the tremendous amount of information available can be overwhelming. Therefore, we have to gather more preferences from recommenders and aggregate them to gain consensuses. For an example of the preference ranking, C>A≥D≥B indicates that C is favorable to A, and A (D) is somewhat favorable but not fully favorable to D (B), where > and ≥ are comparators, and A, B, C, and D are items. This shows the ranking relationship between items. However, no studies, to the best of our knowledge, have ever developed a recommendation system to suggest a temporal relationship between items. That is, “item A could occur during the duration of item B” or “item C could occur before item D”. This type of recommendation can be applied to the reading order of books, course plans in colleges, or the order of taking medicine for patients. In this study, we propose a novel recommendation model to discover closed consensus temporal patterns, where closed means that the patterns are only the maximum consensus sequences. Experiments using a real dataset showed the model’s effectiveness. By the results, it can help enterprises to early provide strategies on goods or services efficiently.

並列關鍵字

closed pattern ； group decision making ； data mining ； consensus temporal pattern

參考文獻

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