Cost-effective and interpretable job skill recommendation with deep reinforcement learning

Abstract

Nowadays, as organizations operate in very fast-paced and competitive environments, workforce has to be agile and adaptable to regularly learning new job skills. However, it is nontrivial for talents to know which skills to develop at each working stage. To this end, in this paper, we aim to develop a cost-effective recommendation system based on deep reinforcement learning, which can provide personalized and interpretable job skill recommendation for each talent. Specifically, we first design an environment to estimate the utilities of skill learning by mining the massive job advertisement data, which includes a skill-matching-based salary estimator and a frequent itemset-based learning difficulty estimator. Based on the environment, we design a Skill Recommendation Deep Q-Network (SRDQN) with multi-task structure to estimate the long-term skill learning utilities. In particular, SRDQN recommends job skills in a personalized and cost-effective manner; that is, the talents will only learn the recommended necessary skills for achieving their career goals. Finally, extensive experiments on a real-world dataset clearly validate the effectiveness and interpretability of our approach.