Top-k Sorting Under Partial Order Information

We address the problem of sorting the top-k elements of a set, given a predefined partial order over the set elements. Our means to obtain missing order information is via a comparison operator that interacts with a crowd of domain experts to determine the order between two unordered items. The practical motivation for studying this problem is the common scenario where elements cannot be easily compared by machines and thus human experts are harnessed for this task. As some initial partial order is given, our goal is to optimally exploit it in order to minimize the domain experts work. The problem lies at the intersection of two well-studied problems in the theory and crowdsourcing communities:full sorting under partial order information and top-k sorting with no prior partial order information. As we show, resorting to one of the existing state-of-the-art algorithms in these two problems turns out to be extravagant in terms of the number of comparisons performed by the users. In light of this, we present a dedicated algorithm for top-k sorting that aims to minimize the number of comparisons by thoroughly leveraging the partial order information. We examine two possible interpretations of the comparison operator, taken from the theory and crowdsourcing communities, and demonstrate the efficiency and effectiveness of our algorithm for both of them. We further demonstrate the utility of our algorithm, beyond identifying the top-k elements in a dataset, as a vehicle to improve the performance of Learning-to-Rank algorithms in machine learning context. We conduct a comprehensive experimental evaluation in both synthetic and real-world settings.