[tkde2018] we introduce a novel dual generative model, called lam, for workforce analytics. the lam development is novel in three aspects. first, we introduce the concept of latent ability variables to model hidden relations between employees and activities in terms of job performance, such as the set of skills provided by an employee and the set of skills required by an activity, and how well they matchup in employee-activity assignment. second, we construct the latent ability model (lam) by learning latent ability parameters from the employee-activity log data using expectation-maximization and gradient descent. finally, we leverage lam to build inference and prediction models for employee performance prediction, employee ability comparison, and employee-activity matchup quality estimation
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