Top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons

Sina Akbari, Adolfo R. Escobedo

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

Abstract

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
Pages51-63
Number of pages13
ISBN (Print)9783031185298
DOIs
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

Conference

Conference7th International Symposium on Combinatorial Optimization, ISCO 2022
CityVirtual, Online
Period5/18/225/20/22

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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