A Probabilistic Approach to Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons

Abstract

This is an extended study on crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons. The prior study on the same topic demonstrate the framework and algorithms used to determine all the Pareto-Optimal objects with the goal of asking the fewest possible questions to the crowd. One of the drawbacks in that approach is it fails to incorporate every inputs given by the crowd and is biased towards the majority. We have developed an approach which represent the inputs provided by users as probabilistic values rather than a concrete one. The goal of this study is to find the ranks of the objects based on their probability of being Pareto-Optimal by asking every possible questions. We have used the possible world notion to compute these ranks. Further we have also demonstrated the prospect of using Slack (a cloud-based team collaboration tool) as a Crowdsourcing platform.