Abstract-This paper presents the first systematic investigation of the potential performance gains for crowdsourcing systems, deriving from available information at the requester about individual worker earnestness (reputation). In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple "maximum a-posteriori" decision rule. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the LRA decision rule introduced in the literature.
In REF , Tarable et al. proposed a "maximum a-posteriori" decision rule to help the requestor make decisions in reputation-based task allocation for crowdsourcing, which worked well even in the case of inaccurate reputation information.
The importance of being earnest in crowdsourcing systems
2015 IEEE Conference on Computer Communications (INFOCOM)
2015 IEEE Conference on Computer Communications (INFOCOM)
Computer Science