Pairwise versus pointwise ranking: A case study

Vitalik Melnikov, Pritha Gupta, Bernd Frick, Daniel Kaimann, Eyke Hüllermeier

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)73-83
Number of pages11
JournalSchedae Informaticae
Volume25
DOIs
Publication statusPublished - 2016

Bibliographical note

Funding Information:
This work has been supported by the German Research Foundation (Deutsche Forsch-ungsgesellschaft,DFG) within the Collaborative Research Centre “On-The-Fly Computing” (CRC 901).

Research programs

  • ESHCC A&CS

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