  We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked
data, which can take the accuracy levels of different users into account. By
allowing different noise distributions, the proposed HTM model maintains the
generality of Thurstone's original framework, and as such, also extends the
Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous
populations of users. Under this framework, we also propose a rank aggregation
algorithm based on alternating gradient descent to estimate the underlying item
scores and accuracy levels of different users simultaneously from noisy
pairwise comparisons. We theoretically prove that the proposed algorithm
converges linearly up to a statistical error which matches that of the
state-of-the-art method for the single-user BTL model. We evaluate the proposed
HTM model and algorithm on both synthetic and real data, demonstrating that it
outperforms existing methods.
