@inproceedings{6cd0f127e7064d489a4f4863143fdc7b,
title = "Top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons",
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.",
keywords = "Kendall tau distance, Mixed integer programming, Polyhedral analysis, Rank aggregation, Top-k list aggregation",
author = "Sina Akbari and Escobedo, {Adolfo R.}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Symposium on Combinatorial Optimization, ISCO 2022 ; Conference date: 18-05-2022 Through 20-05-2022",
year = "2022",
doi = "10.1007/978-3-031-18530-4_4",
language = "English (US)",
isbn = "9783031185298",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "51--63",
editor = "Ivana Ljubi{\'c} and Francisco Barahona and Dey, {Santanu S.} and Mahjoub, {A. Ridha}",
booktitle = "Combinatorial Optimization - 7th International Symposium, ISCO 2022, Revised Selected Papers",
address = "Germany",
}