Recommender Systems for E-Learning

Abstract

The recommender systems are really important in this phase because the users want to be concentrated and to be focused on the domain in which they are interested. There should be minimal deviation in the topics suggested by the recommendation engines. Some of the famous e-learning platforms suggest recommendations based on tags such as highest rated, bestsellers, and so on in various domains. This ultimately makes the users deviate from the domain in which they have to master, and it results in not satisfying the user needs. So, to address this problem, effective recommendation engines will help provide recommendations according to the users by implementing the machine learning techniques such as collaborative filtering and content-based techniques. In this chapter, the authors discuss the recommendation systems, types of recommendation systems, and challenges.