Course Recommender Systems Need to Consider the Job Market
Jibril Frej Anna Dai Syrielle Montariol Antoine Bosselut Tanja Käser
Current course recommender systems primarily leverage learner-course
interactions, course content, learner preferences, and supplementary course
details like instructor, institution, ratings, and reviews, to make their
recommendation. However, these systems often overlook a critical aspect: the
evolving skill demand of the job market. This paper focuses on the perspective
of academic researchers, working in collaboration with the industry, aiming to
develop a course recommender system that incorporates job market skill demands.
In light of the job market's rapid changes and the current state of research in
course recommender systems, we outline essential properties for course
recommender systems to address these demands effectively, including
explainable, sequential, unsupervised, and aligned with the job market and
user's goals. Our discussion extends to the challenges and research questions
this objective entails, including unsupervised skill extraction from job
listings, course descriptions, and resumes, as well as predicting
recommendations that align with learner objectives and the job market and
designing metrics to evaluate this alignment. Furthermore, we introduce an
initial system that addresses some existing limitations of course recommender
systems using large Language Models (LLMs) for skill extraction and
Reinforcement Learning (RL) for alignment with the job market. We provide
empirical results using open-source data to demonstrate its effectiveness.