Automated Discovery and Classification of Training Videos for Career Progression

Abstract

Job transitions and upskilling are common actions taken by many industry working professionals throughout their career. With the current rapidly changing job landscape where requirements are constantly changing and industry sectors are emerging, it is especially difficult to plan and navigate a predetermined career path. In this work, we implemented a system to automate the collection and classification of training videos to help job seekers identify and acquire the skills necessary to transition to the next step in their career. We extracted educational videos and built a machine learning classifier to predict video relevancy. This system allows us to discover relevant videos at a large scale for job title-skill pairs. Our experiments show significant improvements in the model performance by incorporating embedding vectors associated with the video attributes. Additionally, we evaluated the optimal probability threshold to extract as many videos as possible with minimal false positive rate.