Top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons

Sina Akbari, Adolfo R. Escobedo

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Top-k lists are being increasingly utilized in various fields and applications including information retrieval, machine learning, and recommendation systems. Since multiple top-k lists may be generated by different algorithms to evaluate the same set of entities or system of interest, there is often a need to consolidate this collection of heterogeneous top-k lists to obtain a more robust and coherent list. This work introduces various exact mathematical formulations of the top-k list aggregation problem under the generalized Kendall tau distance. Furthermore, the strength of the proposed formulations is analyzed from a polyhedral point of view.

Original languageEnglish (US)
Title of host publicationCombinatorial Optimization - 7th International Symposium, ISCO 2022, Revised Selected Papers
EditorsIvana Ljubić, Francisco Barahona, Santanu S. Dey, A. Ridha Mahjoub
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031185298
StatePublished - 2022
Event7th International Symposium on Combinatorial Optimization, ISCO 2022 - Virtual, Online
Duration: May 18 2022May 20 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13526 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Symposium on Combinatorial Optimization, ISCO 2022
CityVirtual, Online


  • Kendall tau distance
  • Mixed integer programming
  • Polyhedral analysis
  • Rank aggregation
  • Top-k list aggregation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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