2014 ProfitMaximizingClusterHires

• (Golshan et al., 2014) ⇒ Behzad Golshan, Theodoros Lappas, and Evimaria Terzi. (2014). “Profit-maximizing Cluster Hires.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623690

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• http://scholar.google.com/scholar?q=%222014%22+Profit-maximizing+Cluster+Hires
• http://dl.acm.org/citation.cfm?id=2623330.2623690&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; online marketplaces; team formation

Abstract

Team formation has been long recognized as a natural way to acquire a diverse pool of useful skills, by combining experts with complementary talents. This allows organizations to effectively complete beneficial projects from different domains, while also helping individual experts position themselves and succeed in highly competitive job markets. Here, we assume a collection of [[projects ensuremath{P}]], where each project requires a certain set of skills, and yields a different benefit upon completion. We are further presented with a pool of [[experts ensuremath{X}]], where each expert has his own skillset and compensation demands. Then, we study the problem of hiring a cluster of experts T ⊆ X, so that the overall compensation (cost) does not exceed a given budget B, and the total benefit of the projects that this team can collectively cover is maximized. We refer to this as the ClusterHire problem. Our work presents a detailed analysis of the computational complexity and hardness of approximation of the problem, as well as heuristic, yet effective, algorithms for solving it in practice. We demonstrate the efficacy of our approaches through experiments on real datasets of experts, and demonstrate their advantage over intuitive baselines. We also explore additional variants of the fundamental problem formulation, in order to account for constraints and considerations that emerge in realistic cluster-hiring scenarios. All variants considered in this paper have immediate applications in the cluster hiring process, as it emerges in the context of different organizational settings.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ProfitMaximizingClusterHires	Theodoros Lappas Evimaria Terzi Behzad Golshan			Profit-maximizing Cluster Hires				10.1145/2623330.2623690		2014