Pairwise versus Pointwise Ranking: A Case Study
Vitalik Melnikov, Eyke Hüllermeier, Daniel Kaimann, Bernd Frick, Pritha Gupta · Schedae Informaticae · 2017
Object ranking is one of the most relevant problems in the realm of preference learning and ranking.It is mostly tackled by means of two different techniques, often referred to as pairwise and pointwise ranking.In this paper, we present a case study in which we systematically compare two representatives of these techniques, a method based on the reduction of ranking to binary classification and so-called expected rank regression (ERR).Our experiments are meant to complement existing studies in this field, especially previous evaluations of ERR.And indeed, our results are not fully in agreement with previous findings and partly support different conclusions.