E-learning has gained enormous momentum and is becoming a prominent way of learning
among the student community. A great amount of useful and varied educational resources
are available over the internet through Massive Online Open Courses (MOOCs) websites such
as Coursera, Udacity, EdX, etc. Though students have many opportunities to explore, it
becomes increasingly challenging and time-consuming to search and examine the vast
number of courses for suitable content. To overcome this problem, recommendation systems
can be used. This paper proposes a course recommendation algorithm based on user’s profile
and their similar characteristics to other users. The proposed algorithm combines content-
based filtering with collaborative filtering to provide more accurate and targeted results. For
subjective testing, a web-based system is developed with 8 courses and 15 users.
