A collaborative filtering prediction algorithm for ClassRank subject recommendations

Abstract

Undergraduate students at M.I.T. typically utilize three resources when selecting subjects: course specific evaluations, faculty advisors, and peers. While these resources have distinct advantages, they are all limited in scope. The ClassRank web application has been developed to bridge the gap between these resources by providing a simple institute-wide system for undergraduate students to evaluate and rate subjects. The application also provides a solid platform to build new tools utilizing subject evaluation data. To extend the initial core functionality of the ClassRank system, a rating-based subject recommendation algorithm was added to offer students an unbiased perspective on potential subjects of interest. Developed as a Ruby on Rails plugin and then integrated into ClassRank, the recommendation algorithm analyzes subject ratings and provides personalized suggestions to students about subjects that would likely fit their interests and educational goals. The ClassRank web application and recommendation algorithm will provide the M.I.T. undergraduate student body with a unique and invaluable resource for subject selection.