Learning Personal Traits, Value, Skill Representations for Improved Matching of Jobs, Talent, and Courses

COVID, remote work, remote learning and the accelerated adoption of technology globally has shifted the ways of how we live, learn, and our future lifestyles going forward. The 90% of society face challenges of how best to simplify their navigation and access to knowledge – those who may not have high value networks, and the means and resources to identify and access ways to upskill, reskill, or retrain to enter or re-enter the future workforce. The unprecedented need to be able to navigate of knowledge in career development is currently the greatest challenge to Jobseekers, Employers and Educators. No longer can a jobseeker just rely on just applying to an HR recruitment site or relying on the traditional mode of learning delivery and content. Skill gaps in the workforce are continuing to grow as technology acceleration exceeds the pace of traditional learning deliver models. In attempting to resolve this barrier, this project aims to develop latent representation learning models to improve the job-talent, job-course, and talent-course matching for FutureCite, a site offering one-stop services for empowering and connecting people to careers, companies, courses, and inspiring stories. The proposed models would leverage information provided by stakeholders, including answers of talents.

Faculty Supervisor:

Yuan Tian

Student:

Partner:

FutureCite Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Queen's University

Program: