Stable Job Assignment for Crowdsourcing

In crowdsourcing systems, job assignment is one of the fundamental concerns. Existing works address the job assignment problem solely from the perspective of the crowdsourcer, aiming at maximizing the utility or minimizing the cost for the crowdsourcer. In this paper, we take into consideration users' preferences towards different jobs, and propose a novel matching framework for job assignment in crowdsourcing systems. Assigning multiple users to the same job will improve its quality, however, the crowdsourcer has to guarantee that the total payment to these users is less than the budget of this job. As users ask for different payments due to heterogeneity in their quality levels, classic deferred acceptance algorithm cannot reach a stable job assignment. In this paper, we formulate the job assignment problem in crowdsourcing systems as a many-to-one matching with budget constraints. Then, we design an algorithm that produces a stable job assignment in spite of user heterogeneity. Simulation results show that the proposed job assignment algorithm yields a high average job quality with a low computational complexity.