Skill-based Career Path Modeling and Recommendation

Aritra Ghosh, Beverly Park Woolf, Shlomo Zilberstein, Andrew S. Lan

Abstract: The development of new technologies at an unprecedented rate is rapidly changing the landscape of the labor market. Therefore, for workers who want to build a successful career, acquiring new skills required by new jobs through lifelong learning is crucial. In this paper, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals. Specifically, we use a series of binary-valued and non-decreasing latent states to represent the expanding skill set of each user throughout their career and propose an efficient inference method under our model. Using a series of experiments on two large real-world datasets, we show that our model (sometimes significantly) outperforms existing methods on the tasks of company, job title, and skill prediction. More importantly, our model is interpretable and can be used for other important tasks including skill gap identification and career path planning. Using a series of case studies, we show that our model can provide i) actionable feedback to users and guide them through their upskilling and reskilling processes and ii) recommendations of feasible paths for users to reach their career goals.