Personalized task recommendation in crowdsourcing information systems - Current state of the art

Crowdsourcing information systems are socio-technical systems that provide informational products or services by harnessing the diverse potential of large groups of people via the Web. Interested individuals can contribute to such systems by selecting among a wide range of open tasks. Arguing that current approaches are suboptimal in terms of matching tasks and contributors' individual interests and capabilities, this article advocates the introduction of personalized task recommendation mechanisms. We contribute to a conceptual foundation for the design of such mechanisms by conducting a systematic review of the corresponding academic literature. Based on the insights derived from this analysis, we identify a number of issues for future research. In particular, our findings highlight the need for more significant empirical results through large-scale online experiments, an improved dialog with mainstream recommender systems research, and the integration of various sources of knowledge that exceed the boundaries of individual systems.