JobComposer: Career Path Optimization via Multicriteria Utility Learning
This addresses the need for long-term career guidance on online professional networks, offering an automated solution to improve workforce employability, though it is incremental as it builds on existing data-driven optimization methods.
The paper tackles the problem of automating career path planning by proposing JobComposer, a data-driven approach that learns to optimize career trajectories based on multiple payoff criteria, and it demonstrates better performance than baseline methods and actual career paths in studies using an online professional network dataset.
With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.) becoming popular on the web, people are now turning to these platforms to create and share their professional profiles, to connect with others who share similar professional aspirations and to explore new career opportunities. These platforms however do not offer a long-term roadmap to guide career progression and improve workforce employability. The career trajectories of OPN users can serve as a reference but they are not always optimal. A career plan can also be devised through consultation with career coaches, whose knowledge may however be limited to a few industries. To address the above limitations, we present a novel data-driven approach dubbed JobComposer to automate career path planning and optimization. Its key premise is that the observed career trajectories in OPNs may not necessarily be optimal, and can be improved by learning to maximize the sum of payoffs attainable by following a career path. At its heart, JobComposer features a decomposition-based multicriteria utility learning procedure to achieve the best tradeoff among different payoff criteria in career path planning. Extensive studies using a city state-based OPN dataset demonstrate that JobComposer returns career paths better than other baseline methods and the actual career paths.