大眾分類系統提供了使用者自己定義以及分享各種資源的一種平台，近年來隨著技術上的演進，使用者與使用者之間的社群關係也越來越密切，如何在此類社群關係下找到具有領導指標的物件已經成為一個重要主題。以往過去在這方面的主題多偏向於研究找到高度熱門的網頁或者是專家。基於這點，我們會重新討論有關使用者的程度並提出了關於使用者眼光的新定義。首先在大眾分享系統內，能將網頁分成三種型態: 孤島類型、眾所皆知類型、以及急速發展類型。在我們的定義中，一個有眼光的使用者總是能比大家還早發現一個好網頁，如此一來找到一個會在將來急速發展網頁的價值將會高於一個眾所皆知類型的網頁。在我們的論文中，會先根據網頁時間序列的特性，建立一個演算法去估計使用者存取網頁所能得到的眼光值。另外我們會探討有關使用者建立在粉絲系統網上的社群關係，這關係到一個網頁是否能夠發展快速的另外一種途徑，之後我們會提出一個基於連結關係的方法對使用者眼光以及網頁發展性之間討論互相的影響。最後在我們實驗裡，將提出幾種驗證方式以比較我們的方法CAIS與過去的做法在效果上有何差異性，我們會針對找到網頁的排名以及模擬使用者在網頁時間軸上的分佈做分析與比較，在實驗裡我們會結合幾種方法以驗證在各種情況下，各類方法之間的差異性, 最後我們會從實驗結果中，驗證出我們的方法CAIS不論是找尋模擬的專家使用者分佈，或者是預測未來網頁發展上都有較好的結果。

Folksonomy systems bring a way for users to share and organize bookmarks. The social relation between users and users become stronger with the rapid development of new technologies. How to find the leadership of objects has become an important topic. This theme of research always centralizes in finding the great popular pages or experts. We reform the definition of expertise and propose a new notion which we call user insight. At first, we refer the major three types of page to address the issue; we describe the three general types of page state in our definition, namely isolated, well known, and burgeoning. A user with good insight can always find a good page before than others do in our definition, it occurrence that the contribution of finding a burgeoning page is more than a well known page. In our paper, we build a time serial based algorithm to estimate the user insight. In addition, we discuss the social relationship in fans network, and proposed a linked based algorithm CAIS (Community based annotation Insight Search) to the reinforcement of users and pages. Finally, we design several experiments to evaluate the performance of methods and compare with other baseline ranking approaches. We prove that CAIS has the better performance by showing a simulation case of user ranking and the real case data prediction experiments with different setting.

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