Heterogeneous Knowledge Graph Representation and Learning for Career Path Recommendation

This project is about better understanding talents and managers to help them reach their professional goals. For professionals, it might be about growing their career, getting their dream job, or finding the most efficient way of getting it. For managers, it might be about discovering interesting talents to join their team, or finding the most efficient talent set to carry out their project. To do so, we build a large knowledge graph based on several ontologies describing skills, roles knowledge and abilities. We then use deep learning approaches to identify entities of interest in CVs, infer users’ skills and recommend career moves. As such, our work contributes to the company’s vision of fostering professional growth by assisting organizations of all sizes through their process of digital transformatio

Faculty Supervisor:

Amal Zouaq

Student:

Partner:

Conova AI

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

Polytechnique Montréal

Program: