Rankr: A Mobile System for Crowdsourcing Opinions

Evaluating large sets of items, such as business ideas, is a difficult task. While no one person has time to evaluate all the items, many people can contribute by each evaluating a few. Moreover, given the mobility of people, it is useful to allow them to evaluate items from their mobile devices. We present the design and implementation of a mobile service, Rankr, which provides a lightweight and efficient way to crowdsource the relative ranking of ideas, photos, or priorities through a series of pairwise comparisons. We discover that users prefer viewing two items simultaneously versus viewing one image at a time with better fidelity. Additionally, we developed an algorithm that determines the next most useful pair of candidates a user can evaluate to maximize the information gained while minimizing the number of votes required. Voters do not need to compare and manually rank all of the candidates.