  Consider the following problem faced by an online voting platform: A user is
provided with a list of alternatives, and is asked to rank them in order of
preference using only drag-and-drop operations. The platform's goal is to
recommend an initial ranking that minimizes the time spent by the user in
arriving at her desired ranking. We develop the first optimization framework to
address this problem, and make theoretical as well as practical contributions.
On the practical side, our experiments on Amazon Mechanical Turk provide two
interesting insights about user behavior: First, that users' ranking strategies
closely resemble selection or insertion sort, and second, that the time taken
for a drag-and-drop operation depends linearly on the number of positions
moved. These insights directly motivate our theoretical model of the
optimization problem. We show that computing an optimal recommendation is
NP-hard, and provide exact and approximation algorithms for a variety of
special cases of the problem. Experimental evaluation on MTurk shows that,
compared to a random recommendation strategy, the proposed approach reduces the
(average) time-to-rank by up to 50%.
