Automated Discovery and Classification of Training Videos for Career Progression
This work addresses the challenge for job seekers in navigating career transitions by providing automated tools to identify necessary skills, though it appears incremental as it builds on existing classification methods.
The authors tackled the problem of helping job seekers find relevant training videos for career progression by implementing a system that automates video collection and classification, achieving significant performance improvements through the use of embedding vectors and optimizing probability thresholds to maximize video discovery with minimal false positives.
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.