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Ranking a set of objects involves establishing an order allowing forcomparisons between any pair of objects in the set. Oftentimes, due to theunavailability of a ground truth of ranked orders, researchers resort toobtaining judgments from multiple annotators followed by inferring the groundtruth based on the collective knowledge of the crowd. However, the aggregationis often ad-hoc and involves imposing stringent assumptions in inferring theground truth (e.g. majority vote). In this work, we proposeExpectation-Maximization (EM) based algorithms that rely on the judgments frommultiple annotators and the object attributes for inferring the latent groundtruth. The algorithm learns the relation between the latent ground truth andobject attributes as well as annotator specific probabilities of flipping, ametric to assess annotator quality. We further extend the EM algorithm to allowfor a variable probability of flipping based on the pair of objects at hand. Wetest our algorithms on two data sets with synthetic annotations and investigatethe impact of annotator quality and quantity on the inferred ground truth. Wealso obtain the results on two other data sets with annotations frommachine/human annotators and interpret the output trends based on the datacharacteristics.

Inferring object rankings based on noisy pairwise comparisons from multiple annotators