A VOS analysis of LSTM Learners Classification for Recommendation System

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Published: Jan 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2s.6043
Keywords:
LSTM, Deep Learning, Artificial Intelligence, Machine Learning, recommendation system, VOS Software

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M. Jahir Pasha
Associate Professor, Department of CSE, G. Pullaiah College of Engineering and Technology, Kurnool
Channapragada Rama Seshagiri Rao
Department of CSE ,Professor & Principal ,Vignana Bharathi Engineering College Chintapalliguda(v) Ibrahimpatnam
A. Geetha
Assistant Professor, Department:-CSIT ,-CVR College Of Engineering, MangalPalli,Hyderabad
Terrance Frederick Fernandez
Professor ,Institute of CSE ,Saveetha school of Engineering, SIMATS
Y. Krishna Bhargavi
Associate Professor, Department of CSE ,Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India

Abstract

In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research.

Article Details

How to Cite
Pasha, M. J. ., Rao, C. R. S. ., Geetha, A. ., Fernandez, T. F. ., & Bhargavi, Y. K. . (2023). A VOS analysis of LSTM Learners Classification for Recommendation System. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 179–187. https://doi.org/10.17762/ijritcc.v11i2s.6043
Issue
Vol. 11 No. 2s (2023)
Section
Articles

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