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In heterogeneous rank aggregation problems, users often exhibit variousaccuracy levels when comparing pairs of items. Thus a uniform querying strategyover users may not be optimal. To address this issue, we propose anelimination-based active sampling strategy, which estimates the ranking ofitems via noisy pairwise comparisons from users and improves the users' averageaccuracy by maintaining an active set of users. We prove that our algorithm canreturn the true ranking of items with high probability. We also provide asample complexity bound for the proposed algorithm which is better than that ofnon-active strategies in the literature. Experiments are provided to show theempirical advantage of the proposed methods over the state-of-the-artbaselines.

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons