Demand-Aware Career Path Recommendations: A Reinforcement Learning Approach

Abstract

A skill's value depends on dynamic market conditions. To remain marketable, contractors need to keep reskilling themselves continuously. But choosing new skills to learn is an inherently hard task: Contractors have very little information about current and future market conditions, which often results in poor learning choices. Recommendation frameworks could reduce uncertainty in learning choices. However, conventional approaches would likely be inefficient; they would model previous (often poor) observed contractor learning behaviors to provide future career path recommendations while ignoring current market trends. This work proposes a framework that combines reinforcement learning, Bayesian inference, and gradient boosting to provide recommendations on how contractors should behave when choosing new skills to learn. Compared with standard recommender systems, this framework does not learn from previous (often poor) behaviors to make future recommendations. Instead, it relies on a Markov Decision Process to operate on a graph of feasible actions and dynamically recommend profitable career paths. The framework uses market information to identify current trends and project future wages. Based on this information, it recommends feasible, relevant actions that a contractor can take to learn new, in-demand skills. Evaluation of the framework on 1.73 million job applications from an online labor market shows that its implementation could increase (1) the marketplace's revenue by up to 6%, (2) contractors' wages by 22%, and (3) the diversity of new skill acquisitions by 47%. A comparison with alternative recommender systems highlights the limitations of approaches that make recommendations based on previously observed learning behaviors.