The binary representation of preference information is called preference relation. For each pair of alternatives (instances or labels), a binary predicate can be learned by conventional supervisingsupervised learning approach. Fürnkranz and Hüllermeier proposed this approach in label ranking problem.<ref name=":0">{{Cite journal |last=Fürnkranz |first=Johannes |last2=Hüllermeier |first2=Eyke |date=2003 |editor-last=Lavrač |editor-first=Nada |editor2-last=Gamberger |editor2-first=Dragan |editor3-last=Blockeel |editor3-first=Hendrik |editor4-last=Todorovski |editor4-first=Ljupčo |title=Pairwise Preference Learning and Ranking |url=https://link.springer.com/chapter/10.1007/978-3-540-39857-8_15 |journal=Machine Learning: ECML 2003 |language=en |___location=Berlin, Heidelberg |publisher=Springer |pages=145–156 |doi=10.1007/978-3-540-39857-8_15 |isbn=978-3-540-39857-8}}</ref> For object ranking, there is an early approach by Cohen et al.<ref>{{Cite journal |last=Cohen |first=William W. |last2=Schapire |first2=Robert E. |last3=Singer |first3=Yoram |date=1998-07-31 |title=Learning to order things |url=https://dl.acm.org/doi/10.5555/302528.302736 |journal=NeurIPS |series= |___location=Cambridge, MA, USA |publisher=MIT Press |pages=451–457 |doi= |isbn=978-0-262-10076-2}}</ref>
Using preference relations to predict the ranking will not be so intuitive. Since observed preference relations may not always be transitive due to inconsistencies in the data, finding a ranking that satisfies all the preference relations may not be possible or may result in multiple possible solutions. A more common approach is to find a ranking solution which is maximally consistent with the preference relations. This approach is a natural extension of pairwise classification.<ref name=":0" />
