Elsevier

Discrete Applied Mathematics

Volume 252, 10 January 2019, Pages 28-36
Discrete Applied Mathematics

Rank aggregation: New bounds for MCx

https://doi.org/10.1016/j.dam.2017.07.020Get rights and content
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Abstract

The rank aggregation problem has received significant recent attention within the computer science community. Its applications today range far beyond the original aim of building metasearch engines to problems in machine learning, recommendation systems and more. Several algorithms have been proposed for these problems, and in many cases approximation guarantees have been proven for them. However, it is also known that some Markov chain based algorithms (MC1, MC2, MC3, MC4) perform extremely well in practice, yet had no known performance guarantees. We prove supra-constant lower bounds on approximation guarantees for all of them. Nevertheless, we show that in particular ways, MC4 can be seen as a generalization of Copeland score.

Keywords

Rank aggregation
Computations on discrete structures
Combinatorial algorithms
Approximation guarantees

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