International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

E-Learning Recommender System for Learners: A Machine Learning based Approach

Kamika Chaudhary
Department of Computer Science, Gurukul Kangri Vishwavidyalaya, Haridwar, India.

Neena Gupta
Department of Computer Science, Gurukul Kangri Vishwavidyalaya, Haridwar, India.


Received on September 15, 2018
Accepted on April 04, 2019


Web mining procedure helps the surfers to get the required information but finding the exact information is as good as finding a needle in a haystack. In this work, an intelligent prediction model using Tensor Flow environment for Graphics Processing Unit (GPU) devices has been designed to meet the challenges of speed and accuracy. The proposed approach is isolated into two stages: pre-processing and prediction. In the first phase, the procedure starts via looking through the URLs of various e-learning sites particular to computer science subjects. At that point, the content of looked through URLs are perused and after that from their keywords are produced identified with a particular subject in the wake of playing out the pre-processing of the content. Second phase is prediction that predicts query specific links of e-learning website. The proposed Intelligent E-learning through Web (IEW) has content mining, lexical analysis, classification and machine learning based prediction as its key features. Algorithms like SVM, Naïve Bayes, K-Nearest Neighbor, and Random Forest were tested and it was found that Random Forest gave an accuracy of 98.98%, SVM 42%, KNN 63% and Naïve Bayes 66%. Based on the results IEW uses Random forest for prediction.

Keywords- Web mining, Machine learning, Lexical analysis, Prediction modelling, GPU, Tensor flow.


Chaudhary, K., & Gupta, N. (2019). E-Learning Recommender System for Learners: A Machine Learning based Approach. International Journal of Mathematical, Engineering and Management Sciences, 4(4), 957-967.

Conflict of Interest

The authors declare that there is no conflict of interest to declare for this publication.


The authors would like to thank anonymous reviewers for their constructive comments to improve the quality of this paper.


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