A Content Recommendation System for Effective E-Learning using Semantic Fuzzy Humming birds Optimization and RoBERTa
|© 2022 by IJETT Journal|
|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
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
E-learning, LSTM, Content recommendation system, Fuzzy optimality, and Optimization.
 Hadiezaldeen, Rachitamisra, Sukantkishorobisoy, Rawaaalatrash, Rojalina Priyadarshini, "A Hybrid E-Learning Recommendation Integrating Adaptive Profiling and Sentiment Analysis," Journal of Web Semantics, Vol.72, No. 100700, Pp. 1-18, 2022.
 Nur W.Rahayu, Ridiferdiana, Sri S.Kusumawardani, "A Systematic Review of Ontology Use in E-Learning Recommender System," Computers and Education: Artificial Intelligence, Vol.3, No.100047, Pp.1-16, 2022.
 Miao Li, Xuguang Bao, Liang Chang, Tianlong Gu, "Modeling Personalized Representation for Within-Basket Recommendation Based on Deep Learning," Expert Systems with Applications, Vol. 192, No.116383, Pp. 1-21, 2022.
 Mansourehghiasabadi Farahani, Javad Akbari Torkestani, Mohsen Rahmani, "Adaptive Personalized Recommender System Using Learning Automata and Items Clustering," Information Systems, Vol.106, No.101978, Pp. 1-19, 2022.
 Idris Rabiu, Naomie Salim, Aminu Da'u, Maged Nasser, "Modeling Sentimental Bias and Temporal Dynamics for Adaptive Deep Recommendation System," Expert Systems with Applications, Vol.191, No.116262, Pp. 1-15, 2022.
 Xin Huang, Hongyu Hou, Mingyao Sun, "A Novel Temporal Recommendation Method Based on User Query Topic Evolution," Knowledge-Based Systems, No. 108239, Pp. 1-21, 2022.
 Sunny Sharma, Vijay Rana, Vivek Kumar, "Deep Learning Based Semantic Personalized Recommendation System," International Journal of Information Management Data Insights, Vol.1, No.2, No.100028, Pp.1-7, 2021.
 Sadia Ali, Yaser Hafeez, Mamoona Humayun, Nor Shahida Mohd Jamail, Muhammad Aqib, Asif Nawaz, "Enabling Recommendation System Architecture in Virtualized Environment for E-Learning," Egyptian Informatics Journal, Pp.1-13, 2021
 Ravita Mishra, Sheetal Rathi, "Enhanced DSSM (Deep Semantic Structure Modelling) Technique for Job Recommendation," Journal of King Saud University - Computer and Information Sciences, 2021.
 Abdelaziz Khaled, Samir Ouchani, Chemseddinechohra, "Recommendations-Based on Semantic Analysis of Social Networks in Learning Environments," Computers in Human Behavior, Vol.101, Pp. 435-449, 2019.
 Jeevamol Joy, Renumolvemballiveligovinda Pillai, "Review and Classification of Content Recommenders in E-Learning Environment," Journal of King Saud University - Computer and Information Sciences, 2021.
 Aminu Dau, Naomie Salim, Rabiu Idris, "An Adaptive Deep Learning Method for Item Recommendation System," KnowledgeBased Systems, Vol.213, No.106681, Pp. 1-16, 2021.
 Anatoly Gladun. Julia Rogushina, Francisco Garcı´A-Sanchez, Rodrigo Martínez-Béjar, Jesualdo Tomás Fernández-Breis, "An Application of Intelligent Techniques and Semantic Web Technologies in E-Learning Environments", Expert Systems with Applications, Vol.36, No.2, Part 1, Pp.1922-1931, 2009.
 Aminu Da'u, Naomie Salim, Idris Rabiu, Akram Osman, "Recommendation System Exploiting Aspect-Based Opinion Mining with Deep Learning Method," Information Sciences, Vol.512, Pp.1279-1292, 2020.
 Shimaaouf, Mahmoud Abd Ellatif, S.E.Salama, Yehia Helmy, "A Proposed Paradigm for Smart Learning Environment Based on Semantic Web," Computers in Human Behavior, Vol.72, Pp. 796-818, 2017.
 Xiaofei Zhu, Jie Wu, Ling Zhu, Jiafeng Guo, Ran Yu, Katarina Boland, Stefan Dietze, "Exploring User Historical Semantic and Sentiment Preference for Microblog Sentiment Classification," Neurocomputing, Vol.464, Pp. 141-150, 2021.
 R.V.Karthik, Sannasi Ganapathy, "A Fuzzy Recommendation System for Predicting The Customers Interests Using Sentiment Analysis and Ontology in E-Commerce," Applied Soft Computing, Vol.108, No. 107396, Pp. 1-21, 2021.
 Seungyeon Lee, Dohyun Kim, "Deep Learning Based Recommender System Using Cross Convolutional Filters," Information Sciences, 2022.
 Feng Zou, Debao Chen, Qingzheng Xu, Ziqi Jiang, Jiahui Kang, ", "A Two-Stage Personalized Recommendation Based on MultiObjective Teaching–Learning-Based Optimization with Decomposition," Neurocomputing, Vol.452, Pp.716-727, 2021.
 C.De Maio, G.Fenza, M.Gaeta, V.Loia, F.Orciuoli, S.Senatore, "RSS-Based E-Learning Recommendations Exploiting Fuzzy FCA for Knowledge Modeling," Applied Soft Computing, Vol.12, No.1, Pp.113-124, 2012.
 Fei Cai, Maartenderijke, "Learning From Homologous Queries and Semantically Related Terms for Query Auto Completion," Information Processing & Management, Vol.52, No.4, Pp.628-643, 2016.
 Xin Huang, Hongyu Hou, Mingyao Sun, "A Novel Temporal Recommendation Method Based on User Query Topic Evolution," Knowledge-Based Systems, No. 108239, 2022.
 A. Garrido and L. Morales, "E-Learning and Intelligent Planning: Improving Content Personalization," in IEEE Revistaiberoamericana De Tecnologias Del Aprendizaje, Vol. 9, No. 1, Pp. 1-7, Feb. 2014.
 S. M. Nafea, F. Siewe and Y. He, "on Recommendation of Learning Objects Using Felder-Silverman Learning Style Model," in IEEE Access, Vol. 7, Pp. 163034-163048, 2019.
 S. Shishehchi, S. Y. Banihashem and N. A. M. Zin, "A Proposed Semantic Recommendation System for E-Learning: A Rule and Ontology Based E-Learning Recommendation System," 2010 International Symposium on Information Technology, Pp. 1-5, 2010.
 D. Wu, J. Lu and G. Zhang, "A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System," in IEEE Transactions on Fuzzy Systems, Vol. 23, No. 6, Pp. 2412-2426, 2015.
 R. Rajkumar and V. Ganapathy, "Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase The Efficiency of E-Learning," in IEEE Access, Vol. 8, Pp. 67377-67395, 2020.
 L. Anido, M. Llamas, M. Caeiro, J. Santos, J. Rodriguez and M. J. Fernandez, "an Update on The Simulnet Educational Platform. Towards Standards-Driven E-Learning," in IEEE Transactions on Education, Vol. 44, No. 2, Pp. 6 ,2001.
 F. Colace and M. De Santo, "Ontology for E-Learning: A Bayesian Approach," in IEEE Transactions on Education, Vol. 53, No. 2, Pp. 223-233,2010.
 M. M. Brut, F. Sedes and S. D. Dumitrescu, "A Semantic-Oriented Approach for Organizing and Developing Annotation for ELearning," in IEEE Transactions on Learning Technologies, Vol. 4, No. 3, Pp. 239-248, 2011.
 P. A. Khodke, M. G. Tingane, A. P. Bhagat, S. P. Chaudhari and M. S. Ali, "Neuro Fuzzy Intelligent E-Learning Systems," 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Pp. 1-7, 2016.
 K. O. Gogo, L. Nderu and R. W. Mwangi, "Fuzzy Logic Based Context Aware Recommender for Smart E-Learning Content Delivery," 2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI), Pp. 114-118, 2018.
 SP Perumal, G Sannasi, K Arputharaj, "An Intelligent Fuzzy Rule-Based E-Learning Recommendation System for Dynamic User Interests," The Journal of Supercomputing, Vol. 75, No. 8, Pp. 5145-5160, 2019.
 RV Karthik, S Ganapathy, "A Fuzzy Recommendation System for Predicting The Customers Interests Using Sentiment Analysis and Ontology in E-Commerce," Applied Soft Computing 108, 107396, 2021.
 S Munuswamy, MS Saranya, S Ganapathy, S Muthurajkumar, A Kannan, "Sentiment Analysis Techniques for Social Media-Based Recommendation Systems," National Academy Science Letters, Vol. 44, No.3, Pp. 281-287, 2021.