Graduation Semester and Year
2017
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Chengkai Li
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.
Keywords
Crowdsourcing, Pareto-Optimal Objects, Probability
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Shakya, Nigesh, "A Probabilistic Approach to Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons" (2017). Computer Science and Engineering Theses. 444.
https://mavmatrix.uta.edu/cse_theses/444
Comments
Degree granted by The University of Texas at Arlington