@article{b4722bcf-0959-4035-9a7e-ef539d07c80e,
author = {Vitalik Melnikov, Pritha Gupta, Bernd Frick, Daniel Kaimann, Eyke Hüllermeier},
title = {Pairwise versus Pointwise Ranking: A Case Study},
journal = {Schedae Informaticae},
volume = {2016},
number = {Volume 25},
year = {2017},
issn = {1732-3916},
pages = {73-83},keywords = {Preference learning; object ranking; linear regression; logistic regression; hotel rating; TripAdvisor},
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.},
doi = {10.4467/20838476SI.16.006.6187},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/pairwise-versus-pointwise-ranking-a-case-study}
}