A Content Recommendation System for Effective E-Learning using Semantic Fuzzy Humming birds Optimization and RoBERTa

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Manikandan N K, Kavitha M
DOI : 10.14445/22315381/IJETT-V70I7P221

How to Cite?

Manikandan N K, Kavitha M, "A Content Recommendation System for Effective E-Learning using Semantic Fuzzy Humming birds Optimization and RoBERTa" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 198-211, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P221

Abstract
E-learning plays a major role in this pandemic for learning the subjects in depth through self-learning. In this e-learning process, the study materials are useful for the learners to learn their interested subjects deeply with good understanding. Identifying suitable and most useful materials is very difficult today due to the availability of huge relevant materials online. For identifying suitable study materials, the various content recommendation systems are available for providing suitable study materials to the learners. Even though the available content recommendation systems are not fulfilling the current requirements, for this purpose, we propose a new content recommendation system that applies a newly proposed semantic fuzzy optimality aware hummingbirds optimization technique and the enhanced version of the Bidirectional Encoder Representations from Transformers (BERT) called Robustly optimized BERT Pretrained Approach (RoBERTa)for identifying the more relevant content to the e-learners according to their interests and learning capability. In this work, the semantic similarity score is calculated for each study material and considered the fuzzy optimality result as input for the hummingbirds' optimization technique for identifying the more relevant terms that are meaningful and used to find suitable study materials. Finally, the RoBERTa is applied for categorizing the relevant, irrelevant, and most useful documents from the available online, local repository, and dataset. The experiments have been conducted to evaluate the proposed system and proved that as better than the existing systems in terms of precision, recall, f1-measure, and prediction accuracy

Keywords
E-learning, LSTM, Content recommendation system, Fuzzy optimality, and Optimization.

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