A Recommendation System for University Discussion Committees

One of the topics that have emerged and gained popularity in recent years, due to the extensive availability of data, is recommendation systems. The concept of recommendation systems is based on saving users' time and effort while using the Internet for browsing, shopping, or other web activities. On the other hand, one of the routine tasks that is consistently performed in the academic community is the selection of committees’ members for the defense of master's thesis or doctoral dissertations. These committees are responsible for evaluating the graduate students’ work and assessment of the academic and research efforts. In general, naming discussion committees' members is one of the challenges that used to be solved manually. In this work, a recommendation system was built to propose a discussion committee’s members at Computer Science department in University of Mosul based on a dataset that includes the committees that were previously named. Two methods were introduced, developed, and tested based on content-based recommendation system techniques and cluster-based recommendation system techniques.