Towards a Distributed Worker-Job Matching Architecture for Crowdsourcing

Abstract

While the crowd sourcing paradigm facilitates the use of human-enacted resources from large groups of individuals, matching workers with jobs is limited by the need for these potential workers to proactively subscribe to various networks. This subscription phase is part of an "open call model" that reduces the ability for crowd sourcing platforms to scale or retain crowd-oriented workers. Leveraging collaborative filtering techniques, in this paper, we propose an alternative model that seeks to address the issue through a recommendation technique and system that exploits a push-pull model.