  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.
