  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.
