SoSTA: Skill-Oriented Stable Task Assignment With Bidirectional Preferences in Crowdsourcing

Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform's long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall utility while following skill and budget constraints. Our study redefines the general utility of the platform as an amalgamation of utilities on both the workers' and tasks' sides, incorporating the preference lists of each worker or task based on their respective utility scores for the other party. SoSTA incorporates Multi Skill-oriented Stable Worker Task Mapping (Multi-SoS-WTM) algorithm for contributions with multiple skills per worker. SoSTA is rational, non-wasteful, fair, and hence stable. SoSTA outperformed other approaches in the simulations of the MeetUp dataset. SoSTA improves execution speed by 80%, task completion rate by 60%, and user happiness by 8%.