Crowdsourced top-k queries by pairwise preference judgments with confidence and budget control

Abstract

Crowdsourced query processing is an emerging technique that tackles computationally challenging problems by human intelligence. The basic idea is to decompose a computationally challenging problem into a set of human-friendly microtasks (e.g., pairwise comparisons) that are distributed to and answered by the crowd. The solution of the problem is then computed (e.g., by aggregation) based on the crowdsourced answers to the microtasks. In this work, we attempt to revisit the crowdsourced processing of the top-k queries, aiming at (1) securing the quality of crowdsourced comparisons by a certain confidence level and (2) minimizing the total monetary cost. To secure the quality of each paired comparison, we employ statistical tools to estimate the confidence interval from the collected judgments of the crowd, which is then used to guide the aggregated judgment. We propose novel frameworks, SPR and SPR $$^+$$ , to address the crowdsourced top-k queries. Both SPR and SPR $$^+$$ are budget-aware, confidence-aware, and effective in producing high-quality top-k results. SPR requires as input a budget for each paired comparison, whereas SPR $$^+$$ requires only a total budget for the whole top-k task. Extensive experiments, conducted on four real datasets, demonstrate that our proposed methods outperform the other existing top-k processing techniques by a visible difference.