近年來，社群問答網站累積了大量的問題以及回答的資訊。像是Yahoo! Answers跟Stack Overflow這種社群問答網站允許讓使用者去根據他們的需求問問題，以及去回答別人的問題。然而，因為問題的量越來越大，我們無法確定使用者的問題是否能夠很快地被其他使用者回答或解決。在我們的研究中，我們主要解決有效地問題導向的問題，我們把問題導向給有能力以及能夠及時回答的使用者讓問題可以被有效地以及快速地被解決。我們相信解決這個問題可以改善社群問答網站的效能以及滿足使用者的需求。這個研究的難度在於我們無法直接套用傳統專家排序的方法在我們的問題上。我們藉由使用者以前的回答紀錄提出了一個計算使用者專業度以及行動力的方法。在專業度模型建構上，我們首先藉由使用者之前回答過的問題內容去衡量其專業度，再透過在社群問答網站中使用者之間的互動去加強專業度的計算；在行動力模型建構上，我們分析使用者以前在網站中活動的記錄及曲線，進而去預測他們在未來的行動力。藉由模擬出使用者的專業度以及行動力，我們就可以將問題導向給那些有能力的使用者，使得問題得到快速的解答。在論文中，我們在一個真實的社群問答網站資料上面進行我們的實驗分析，證實我們的方法可以比其他方法還要好。在MRR的評估標準下，以問題內容為主的效能為0.0999，而我們的方法可達到0.1372。效能改善了約37%。平均來講，一個新問題可以藉由我們的方法，導向給排名前7名的使用者就至少可以得到一個回答。

Community Question Answering (cQA) sites such as Yahoo! Answers and Stack Overflow have emerged as a new type of community portal that allows users to answer the questions asked by other people. The cQA archives have accumulated a huge mass of questions and answers. On account of the progressively increasing questions, there are numbers of questions to be solved or answered by others. In this paper, we address the problem of efficient question routing. We present a new approach that combines user’s expertise and user’s activity to solve this problem. First, we model user’s expertise by the contents of user’s answering questions in the past, and then we enhance user’s expertise by social network characteristic in the cQA portal. Second, we model and predict user’s activity by analyzing the distribution of their previous answering records. Experiments conducted on a real cQA data, Stack Overflow, show that our approach leads to a better performance than other baseline approaches significantly. In terms of the evaluation metric, MRR, the performance of the content-based approach is 0.0999 and that of our method is 0.1372 respectively. We can get a 37.34% improvement over the traditional content-based method. On average, each of 6,160 test questions gets at least one answer if it is routed to the top 7 ranked users by our approach.

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