Publication Type

Conference Proceeding Article

Publication Date



With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.)becoming popular on the web, people are now turning to these platforms tocreate and share their professional profiles, to connect with others who sharesimilar professional aspirations and to explore new career opportunities. Theseplatforms however do not offer a long-term roadmap to guide career progressionand improve workforce employability. The career trajectories of OPN users canserve as a reference but they are not always optimal. A career plan can also bedevised through consultation with career coaches, whose knowledge may howeverbe limited to a few industries. To address the above limitations, we present anovel data-driven approach dubbed JobComposer to automate career path planningand optimization. Its key premise is that the observed career trajectories inOPNs may not necessarily be optimal, and can be improved by learning tomaximize the sum of payoffs attainable by following a career path. At itsheart, JobComposer features a decomposition-based multicriteria utilitylearning procedure to achieve the best tradeoff among different payoff criteriain career path planning. Extensive studies using a city state-based OPN datasetdemonstrate that JobComposer returns career paths better than other baselinemethods and the actual career paths.


Career planning, Multicriteria optimization, Job transition


Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering


Workshop on Data Science for Human Capital Management (DSHCM2018), Dublin, Ireland, Europe, 2018 September 14

City or Country

Dublin, Ireland, Europe

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.