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.

本文提出了一种混合主动抽样策略，利用贝叶斯优化和 Bradley-Terry 模型构建效用函数，并使用高斯 - 好米特积分来评估期望的信息增益，从而恢复稀疏噪声对标。在每次试验中使用全局最大期望信息增益样本或最小生成树样本的混合主动抽样策略，其决定于测试预算。该方法在模拟和实际数据集上均表现出比现有方法更高的偏好聚合能力。