Paper Info
Title
JobComposer: Career Path Optimization via Multicriteria Utility Learning.
Abstract
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.
Year
Venue
Field
2018
arXiv: Social and Information Networks
Motion planning,Employability,Engineering management,Computer science,Workforce,Premise,Career planning,Multi-objective optimization,Artificial intelligence,Delegation (computing),Machine learning,Stochastic game
DocType
Volume
ISSN
Journal
abs/1809.01062
ECML-PKDD Data Science for Human Capital Management 2018
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Richard Jayadi Oentaryo18010.00
Xavier Jayaraj Siddarth Ashok200.34
Ee-Peng Lim35889754.17
Philips Kokoh Prasetyo47310.52