Future of Information and Communication Conference (FICC) 2025
28-29 April 2025
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 6, 2024.
Abstract: In this modern streaming platform, the movie recommendation system is an important tool for enabling the users to find new content specialized to their interests. To address the cold start problem prevalent in movie recommendation systems, we introduce the Long Short-Term Memory-Gaussian Nesterov’s Optimal Gradient (LSTM-GNOG) approach. This model utilizes both implicit and explicit feedback to effectively manage sparse rating data. By integrating Bayesian Personalized Ranking (BPR) and Probabilistic Matrix Factorization (PMF) algorithms with preprocessing via Singular Value Decomposition (SVD), our system enhances data robustness. Our empirical results on the MovieLens 100K, MovieLens 1M, FilmTrust, and Ciao datasets demonstrate significant improvements, with Mean Absolute Error (MAE) values of 0.4962, 0.5249, 0.4625, and 0.5341, respectively. Compared to traditional methods such as Unsupervised Boltzmann Machine-based Time-aware Recommendation (UBMTR) and Efficient Gowers-Jaccard-Sigmoid Measure (EGJSM), LSTM-GNOG shows better improvement in prediction accuracy. These results underscore the effectiveness of LSTM-GNOG in overcoming data sparsity issues in movie recommendations.
Ravikumar R N, Sanjay Jain and Manash Sarkar, “LSTM-GNOG: A New Paradigm to Address Cold Start Movie Recommendation System using LSTM with Gaussian Nesterov’s Optimal Gradient” International Journal of Advanced Computer Science and Applications(IJACSA), 15(6), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150675
@article{N2024,
title = {LSTM-GNOG: A New Paradigm to Address Cold Start Movie Recommendation System using LSTM with Gaussian Nesterov’s Optimal Gradient},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150675},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150675},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Ravikumar R N and Sanjay Jain and Manash Sarkar}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.