Recommender systems utilize the opinions of a residential district of users. It assists individuals for the reason that the community more effectively identifies the content of great interest from a set that is potentially overwhelming. The instructor provides an online course which consists of the learning materials, self-quiz, and learning path in a virtual classroom. Typical learners study course material and do self-quiz so that you can evaluate their knowledge. The essential thing that is important to the success learners relates to the standard of the educational materials that are not only be determined by given materials by the instructor but additionally be determined by other learners’ recommendations. Recommender systems have now been a helpful tool to recommend items in a lot of online systems, including e-learning. However, not much research has been done to gauge the learning effects regarding the learners if they use e-learning with a recommender system. Instead, most of the researchers were concentrating on the recommender system precision in forecasting the learner’s recommendation as opposed to the knowledge gain. The detailed literature review is presented by the various researchers in the Recommender Systems for the Smart E-Learning environment in this survey article.

E-Learning, Education, Recommender System, Big Data Analytics, Machine Learning

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