Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
March 29, 2017 Β· Declared Dead Β· π International Conference on Information and Knowledge Management
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Authors
Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal
arXiv ID
1703.09845
Category
cs.SI: Social & Info Networks
Cross-listed
cs.AI,
cs.IR
Citations
13
Venue
International Conference on Information and Knowledge Management
Last Checked
3 months ago
Abstract
The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and architecture of the statistical modeling system underlying this product. We focus on the unique data mining challenges while designing and implementing the system, and describe the modeling components such as Bayesian hierarchical smoothing that help to compute and present robust compensation insights to users. We report on extensive evaluation with nearly one year of de-identified compensation data collected from over one million LinkedIn users, thereby demonstrating the efficacy of the statistical models. We also highlight the lessons learned through the deployment of our system at LinkedIn.
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