Recommendation System to Propose Final Project Supervisors using Cosine Similarity Matrix

Abstract

- The selection of a supervisor is an important thing and one of the determinants of whether or not a student's final project research is successful. At the location of this research, students select a supervisor by considering his academic records and recommendations from classmates or seniors. Words of mouth dominate their motivation, and many students do not have a basis for their choice. Selection of the best-fit supervisor significantly impacts a student's progression. Students will be more enthusiastic about doing the final project and may get facilitation in their research because the topics of the student projects match the supervisor's interests and ongoing work. This study aims to make a recommendation system that suggests a supervisor for a student. The student fills in the title, abstract, and keywords of his proposal. The system gives suggestions to prospective supervisors by calculating the similarity of the data with titles, abstracts, and keywords of published articles found in Google Scholar. The recommendation system uses the content-based filtering method to produce a list of recommendations. The cosine similarity algorithm calculates how similar the topic proposed by students is to the lecturers' interests. In building a website-based recommendation system, the authors use Django web framework as the backend and ReactJs as the frontend. The application succeeds in suggesting final project supervisors that match lecturers' interests and expertise with students' proposals.