Design and Evaluation of a Course Recommender System Using Content-Based Approach

Abstract

Finding a user relevant information among huge number of data that are available in web is a difficult process. Therefore, an information filtering technique is needed to help the users to find their desired contents. Recommender system is the most famous technique which is used nowadays in many websites to support the suggestions making process. This paper will explain how to design a course recommender system by using kNN and Naïve Bayes classification algorithms, and evaluate their performances. The proposed recommender system follows content-based approach, by building a user profile (model), based on his/her prior knowledge and actions like, enrolling and rating courses, and compare it with courses attributes to generate recommended courses.