ConeRANK: Ranking as Learning Generalized Inequalities

TL;DR

This paper introduces ConeRank, a novel ranking method that learns preferences by modeling generalized inequalities with cones, demonstrating competitive performance on large-scale ranking datasets.

Contribution

The paper presents a new cone-based approach to learning preferences in ranking, formulating the problem as learning proper cones with a pairwise learning algorithm.

Findings

ConeRank effectively models preferences using cone-based inequalities.

The approach is regularized by controlling cone volume.

Experimental results show ConeRank's competitiveness on LETOR 4.0.

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.