# Rank Aggregation: New Bounds for MCx

arXiv: https://arxiv.org/abs/1510.00738 · HTML: https://mlsanity.com/paper/1510.00738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2015.

## Built on by (most cited first, 2 of 2)

- [Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce](https://mlsanity.com/paper/2107.08598/) (2021)
- [A comparative analysis of rank aggregation methods for the partial label ranking problem](https://mlsanity.com/paper/2502.17077/) (2025)

## Abstract (arXiv)

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. We also raise the lower bound for sorting by Copeland score from 3/2 to 2 and prove an upper bound of 11, before showing that in particular ways, MC4 can nevertheless be seen as a generalization of Copeland score.