  In this paper we present a hybrid active sampling strategy for pairwise
preference aggregation, which aims at recovering the underlying rating of the
test candidates from sparse and noisy pairwise labelling. Our method employs
Bayesian optimization framework and Bradley-Terry model to construct the
utility function, then to obtain the Expected Information Gain (EIG) of each
pair. For computational efficiency, Gaussian-Hermite quadrature is used for
estimation of EIG. In this work, a hybrid active sampling strategy is proposed,
either using Global Maximum (GM) EIG sampling or Minimum Spanning Tree (MST)
sampling in each trial, which is determined by the test budget. The proposed
method has been validated on both simulated and real-world datasets, where it
shows higher preference aggregation ability than the state-of-the-art methods.
