Latent ranking analysis using pairwise comparisons in crowdsourcing platforms

In this paper, we propose a probabilistic model for learning multiple latent rankings by using pairwise comparisons. Our novel model can capture multiple hidden rankings underlying the pairwise comparisons. Based on the model, we develop an efficient inference algorithm to learn multiple latent rankings as well as an effective inference algorithm for active learning to update the model parameters in crowdsourcing systems whenever new pairwise comparisons are supplied. The performance study with synthetic and real-life data sets confirms the effectiveness of our model and inference algorithms.