Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies—part II

Eyke Hüllermeier () and Roman Słowiński ()
Additional contact information
Eyke Hüllermeier: LMU Munich
Roman Słowiński: Poznań University of Technology

4OR, 2024, vol. 22, issue 3, No 1, 313-349

Abstract: Abstract This article elaborates on the connection between multiple criteria decision aiding (MCDA) and preference learning (PL), two research fields with different roots and developed in different communities. It complements the first part of the paper, in which we started with a review of MCDA. In this part, a similar review will be given for PL, followed by a systematic comparison of both methodologies, as well as an overview of existing work on combining PL and MCDA. Our main goal is to stimulate further research at the junction of these two methodologies.

Keywords: Preference learning; Preference modelling; Multiple criteria decision aiding; Multiple criteria decision making; Machine learning; 68T05; 90B50; 90B32; 91B06; 91B08 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10288-023-00561-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aqjoor:v:22:y:2024:i:3:d:10.1007_s10288-023-00561-5

Ordering information: This journal article can be ordered from
https://www.springer ... ch/journal/10288/PSE

DOI: 10.1007/s10288-023-00561-5

Access Statistics for this article

4OR is currently edited by Yves Crama, Michel Grabisch and Silvano Martello

More articles in 4OR from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().