ConeRANK: Ranking as Learning Generalized Inequalities
Machine Learning
2012-06-21 v1 Information Retrieval
Abstract
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.
Cite
@article{arxiv.1206.4110,
title = {ConeRANK: Ranking as Learning Generalized Inequalities},
author = {Truyen T. Tran and Duc Son Pham},
journal= {arXiv preprint arXiv:1206.4110},
year = {2012}
}