# Aggregation of pairwise comparisons with reduction of biases

arXiv: https://arxiv.org/abs/1906.03711 · HTML: https://mlsanity.com/paper/1906.03711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons](https://mlsanity.com/paper/2608.10045/) (2026)

## Abstract (arXiv)

We study the problem of ranking from crowdsourced pairwise comparisons. Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous models for aggregation of pairwise comparisons do not focus on modeling such kind of biases. We introduce a new aggregation model factorBT for pairwise comparisons, which accounts for certain factors of pairwise tasks that are known to be irrelevant to the result of comparisons but may affect workers' answers due to perceptual reasons. By modeling biases that influence workers, factorBT is able to reduce the effect of biased pairwise comparisons on the resulted ranking. Our empirical studies on real-world data sets showed that factorBT produces more accurate ranking from crowdsourced pairwise comparisons than previously established models.