Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
This work addresses preference aggregation for applications like recommendation systems, but it appears incremental as it builds on existing Bayesian optimization and Bradley-Terry models.
The paper tackles the problem of recovering underlying ratings from sparse and noisy pairwise preferences by proposing a hybrid active sampling strategy that combines Global Maximum Expected Information Gain and Minimum Spanning Tree sampling. It demonstrates higher preference aggregation ability than state-of-the-art methods on simulated and real-world datasets.
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.