Robust Plackett-Luce model for <italic>k</italic>-ary crowdsourced preferences.

The aggregation of <italic>k</italic>-ary preferences is an emerging ranking problem, which plays an important role in several aspects of our daily life, such as ordinal peer grading and online product recommendation. At the same time, crowdsourcing has become a trendy way to provide a plethora of <italic>k</italic>-ary preferences for this ranking problem, due to convenient platforms and low costs. However, <italic>k</italic>-ary preferences from crowdsourced workers are often noisy, which inevitably degenerates the performance of traditional aggregation models. To address this challenge, in this paper, we present a RObust PlAckett-Luce (ROPAL) model. Specifically, to ensure the robustness, ROPAL integrates the Plackett-Luce model with a denoising vector. Based on the Kendall-tau distance, this vector corrects <italic>k</italic>-ary crowdsourced preferences with a certain probability. In addition, we propose an online Bayesian inference to make ROPAL scalable to large-scale preferences. We conduct comprehensive experiments on simulated and real-world datasets. Empirical results on “massive synthetic” and “real-world” datasets show that ROPAL with online Bayesian inference achieves substantial improvements in robustness and noisy worker detection over current approaches. [ABSTRACT FROM AUTHOR]