%0 Journal Article
%T ConeRANK: Ranking as Learning Generalized Inequalities
%A Truyen T. Tran
%A Duc Son Pham
%J Computer Science
%D 2012
%I arXiv
%X We propose a new data mining approach in ranking documents based on the concept of cone-based generalized inequalities between vectors. A partial ordering between two vectors is made with respect to a proper cone and thus learning the preferences is formulated as learning proper cones. A pairwise learning-to-rank algorithm (ConeRank) is proposed to learn a non-negative subspace, formulated as a polyhedral cone, over document-pair differences. The algorithm is regularized by controlling the `volume' of the cone. The experimental studies on the latest and largest ranking dataset LETOR 4.0 shows that ConeRank is competitive against other recent ranking approaches.
%U http://arxiv.org/abs/1206.4110v1