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Allocation of courses and research students based on faculty’s subject specialization and area of interest has always remained a challenging task for university administration due to the presence of academics’ cross-domain interests, stale faculty resumes at university portals and changing the skill set demands from the industry. Collaborative filtering and content-based recommender systems have already been in use by the industry for recommending things, such as movies, news, restaurants, and shopping items to the users, and however, no one has utilized these off-the-shelf models for enhancing the student experience and improving the quality of higher education in academia. This paper presents a case study showcasing the use of probabilistic topic models for generating recommendations to users in academia through appropriate course allocation and supervisor assignment. The proposed system coined as ScholarLite harnesses the power of machine learning to extract research themes from faculty members’ past publications, mines research interests from their resumes, and combines it with their educational background to generate recommendations for course teaching, research supervision, and industry–academia collaboration. We have shown the recommendation results on real-world data gathered from the higher education commission of the country and demonstrated that the proposed techniques are scalable across various programs offered by the universities and could be deployed in a small budget by universities for automating course and supervisor allocation procedures. The experiments confirm our performance expectation by showing good relevance and objectivity in results, thus making this decision management system more appealing for large-scale deployment and use by academia.

Knowledge Based Recommender System for Academia Using Machine Learning: A Case Study on Higher Education Landscape of Pakistan