Abstract
Currently there are problems such as fuzzy workers’ characteristics and complex human relations existing on many crowdsourcing platforms, which lead to the difficulty in the recommendation of workers to complete tasks on crowdsourcing platforms. Aiming at worker recommendations in categorical tasks on crowdsourcing platforms, this paper proposes a recommendation considering workers’ multi-community characteristics. It takes factors such as worker’s reputation, preference and activity into consideration. Finally, based on the characteristics of community intersections, it recommends Top-N workers. The results show the recommendations generated by the algorithm proposed in this paper performs the best comprehensively.
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Liao, Z., Xu, X., Lan, P., Long, J., Zhang, Y. (2019). A Recommendation of Crowdsourcing Workers Based on Multi-community Collaboration. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_34
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DOI: https://doi.org/10.1007/978-3-030-33702-5_34
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