| Summary: | 碩士 === 國立政治大學 === 資訊科學學系 === 97 === 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.
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