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Volume 5, Issue 1

Title : Smart Recommender System for Online Courses

Author : Kalpana-a, Dr. Renu Bagoria-b and Dr. Praveen Arora-c

Abstract :

This research paper investigates recommender systems for online courses offered on Coursera. We explored two recommendation approaches: popularity-based and content-based. The popularity-based approach recommends courses with high ratings and a large number of enrollments. The content-based approach recommends courses with similar skillsets to a course the user has already shown interest in. In our model, Data Privacy Concerns is taken into consideration that develop recommender systems that ensure user privacy by anonymizing data and providing users control over the information used for recommendations. This is ensured that the recommendation is made on the basis of user choice. It is very important to convince the Learner that the recommended course is the best suited course as per the choice made by the learner. The main focus of the paper is to control with the course dropout rate as a large number of leaners enroll in a course but dropout from the course due to several reasons like lack of interest and other issues.

Keywords: Recommender System, Popularity Based Recommender, Content Based Recommendation, MOOCs

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