Learning skills adjacency representations for optimized reskilling recommendations
Today's fast changing workplace necessitates constant reskilling of the workforce at both the corporate and national level. Current approaches to reskilling depend on manual logic, which can be time-consuming and expensive due to their dependence on manual labour. In this paper, we propose a scalable machine-learning driven alternative by introducing a method to make reskilling recommendations using word embeddings of skill keywords trained on a corpus of historical job listings and resumes. We achieve this by training dense vector embeddings to represent skill keywords using Word2Vec and fine-tuned BERT models, allowing us to make comparisons between skills. Given an individual's current skills, this model is leveraged to identify which skills to prioritize for their development based on their target role and to recommend reskilling plans based on the identified skill gap. The proposed framework has the potential to aid both public and private organizations to better direct their educational resources to individuals.