# Data-driven Rank Breaking for Efficient Rank Aggregation

arXiv: https://arxiv.org/abs/1601.05495 · HTML: https://mlsanity.com/paper/1601.05495/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2016.

## Built on by (most cited first, 10 of 10)

- [Feel-Good Thompson Sampling for Contextual Dueling Bandits](https://mlsanity.com/paper/2404.06013/) (2024)
- [Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits](https://mlsanity.com/paper/2202.11795/) (2022)
- [On the identifiability of mixtures of ranking models](https://mlsanity.com/paper/2201.13132/) (2022)
- [LLM Routing with Dueling Feedback](https://mlsanity.com/paper/2510.00841/) (2025)
- [Dueling Convex Optimization with General Preferences](https://mlsanity.com/paper/2210.02562/) (2022)
- [Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization](https://mlsanity.com/paper/2402.18917/) (2024)
- [Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options](https://mlsanity.com/paper/2510.18713/) (2025)
- [Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling](https://mlsanity.com/paper/2112.15575/) (2021)
- [Fair Active Ranking from Pairwise Preferences](https://mlsanity.com/paper/2402.03252/) (2024)
- [Optimal Top-k Identification from Pairwise Comparisons](https://mlsanity.com/paper/2607.08979/) (2026)

## Abstract (arXiv)

Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied to efficient algorithms tailored for independent paired comparisons. However, due to the ignored dependencies in the data, naive rank-breaking approaches can result in inconsistent estimates. The key idea to produce accurate and consistent estimates is to treat the pairwise comparisons unequally, depending on the topology of the collected data. In this paper, we provide the optimal rank-breaking estimator, which not only achieves consistency but also achieves the best error bound. This allows us to characterize the fundamental tradeoff between accuracy and complexity. Further, the analysis identifies how the accuracy depends on the spectral gap of a corresponding comparison graph.