  This paper addresses the problem of rank aggregation, which aims to find a
consensus ranking among multiple ranking inputs. Traditional rank aggregation
methods are deterministic, and can be categorized into explicit and implicit
methods depending on whether rank information is explicitly or implicitly
utilized. Surprisingly, experimental results on real data sets show that
explicit rank aggregation methods would not work as well as implicit methods,
although rank information is critical for the task. Our analysis indicates that
the major reason might be the unreliable rank information from incomplete
ranking inputs. To solve this problem, we propose to incorporate uncertainty
into rank aggregation and tackle the problem in both unsupervised and
supervised scenario. We call this novel framework {stochastic rank aggregation}
(St.Agg for short). Specifically, we introduce a prior distribution on ranks,
and transform the ranking functions or objectives in traditional explicit
methods to their expectations over this distribution. Our experiments on
benchmark data sets show that the proposed St.Agg outperforms the baselines in
both unsupervised and supervised scenarios.
