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Yayın Learning to rank(Işık Üniversitesi, 2011-04-28) Kılıç, Yasin Ozan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıThe web has grown so rapidly in the last decade and it brought the need for proper ranking. Learning to rank (LTR) is the collection of machine learning technolo- gies that construct a ranking model using training data. The model can sort documents according to their degrees of relevance or preference. In this thesis, we introduce LTR technologies and divide them into three ap- proaches: the point-wise, pair-wise and list-wise. We review the theoritical aspects of each category and introduce the representative algorithms of them. We also introduce a new LTR method GRwC which uses classifîcation and graph algorithms. We reduce the ranking problem to a two class classifîcation problem and apply KNN algorithm on a modified LTR dataset. We compared it with the popular ranking algorithm RankingSVM. Experiments on the well-known ranking datasets show that our proposed method gives slightly worse results than RankingSVM.
