FABJRP, Towards a Fully Automated Bilingual Job Recommendation Platform

Recruitment of future employees is an essential activity in any organization, yet it is tedious and error-prone. Substantial effort is spent in rote tasks like finding candidates matching a particular job offer, contacting them, scheduling an interview and performing the actual interview, while the more interesting tasks like making a final decision on who to hire are more exciting, yet risky. This project aims to explore the extent to which a recruitment platform could fully automated the hiring cycle, by leveraging AI technologies. In particular, we will automatically identify a shortlist of job candidates for a given offer, and make a final recommendation based on the interview responses. The interviews will be performed automatically by an AI chatbot who can engage job candidates and react to changes in emotions (e.g., stress or frustration). This project will help our industrial partner explore the limits of today’s AI and software technologies for AI-based recruitment.

Faculty Supervisor:

Bram Adams;Jinghui Cheng;Amal Zouaq;Jinghui Cheng;Bram Adams

Student:

Partner:

Airudi

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Polytechnique Montréal

Program: