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- Title
LSTM-GNOG: A New Paradigm to Address Cold Start Movie Recommendation System using LSTM with Gaussian Nesterov's Optimal Gradient.
- Authors
Ravikumar R. N.; Jain, Sanjay; Sarkar, Manash
- 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 LSTMGNOG in overcoming data sparsity issues in movie recommendations.
- Subjects
GAUSSIAN distribution; BOLTZMANN machine; BAYESIAN analysis; PROBABILISTIC databases; PREDICTION models
- Publication
International Journal of Advanced Computer Science & Applications, 2024, Vol 15, Issue 6, p746
- ISSN
2158-107X
- Publication type
Article
- DOI
10.14569/ijacsa.2024.0150675