資訊發達的時代，資訊取得的方式與管道比起以前更方便而多元，但龐大資料量同時也造成了我們往往很難找到真正需要資料的問題，也因此資料的排名(ranking)問題就變得十分重要。本研究目的在於運用排名學習找出良好的排名，利用人對於某特定議題所給予的排名順序找出排名規則，並應用於資料探勘上，讓電腦可自動對資料做評分，產生正確的排序，將有助於資料的搜尋。 　　本研究分為兩部分，第一部份為排名演算法的設計，我們改良現有的排名方法(RankBoost)，設計出另一個新的演算法(RealRankBoost)，並且用LETOR benchmark實測，作為與其他方法的比較和效果提升的證明；第二部份為非對稱加權概念的提出，我們考量排名位置所造成的資料被檢視機率不同，而給予不同的權重，使排名結果能更貼近人類的角度。 / With the innovation in computer technology, we have easier ways to access information. But the huge amount of data also makes it hard for us to find what we really want. This is why ranking is important to us. The central issues of many applications are ranking, such as document retrieval, expert finding, and anti spam. The objective of this thesis is to discover a good ranking function according to specific ranking order of the human perceptions. We employ the learning-to-rank approach to automatically score and generate ranking order that helps data searching. This thesis is divided into two parts. Firstly, we design a new learning-to-rank algorithm named RealRankBoost based on an existing method (RankBoost). We investigate the efficacy of the proposed method by performing comparative analysis using the LETOR benchmark. Secondly, we propose to assign asymmetric weightings for ranking in the sense that incorrect placement of top-ranked items should yield higher penalty. Incorporation of the asymmetric weighting technique will further make our system to mimic human ranking strategy.