LSTM-GNOG: A New Paradigm to Address Cold Start Movie Recommendation System using LSTM with Gaussian Nesterov’s Optimal Gradient
R. N. Ravikumar, Sanjay K. Jain, Manash Sarkar · International Journal of Advanced Computer Science and Applications · 2024
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.